Data processing apparatus, data processing method, and program

ABSTRACT

Apparatus, methods and programs for estimating a parameter for modeling a time series signal with stochastic dynamics model, A method for obtaining data representing the mixed signals of two or more time series signals; estimating a parameter for modeling a time series signal with stochastic dynamics model. The present technology relates to a data processing apparatus, a data processing method, and a program.

TECHNICAL FIELD Related Applications

The present application claims the priority benefit of Japanese PriorityPatent Application JP 2012-079579, filed in the Japan Patent Office onMar. 30, 2012, and of Japanese Priority Patent Application JP2012-079580, filed in the Japan Patent Office on Mar. 30, 2012, whichapplications are hereby incorporated by reference in its entirety.

The present technology relates to a data processing apparatus, a dataprocessing method, and a program, and particularly to a data processingapparatus, a data processing method, and a program, capable of easilyand accurately obtaining power consumption or the like of, for example,each of a plurality of electrical appliances in households.

BACKGROUND ART

As a method of presenting power consumption or current consumption of,for example, each of electrical appliances such as household electricalappliances (electrical appliances for households) in a household or thelike to a user in the household and realizing so-called “visualization”of power consumption or the like, there is a method of, for example,installing a smart tap in each outlet.

The smart tap has a measurement function of measuring power which isconsumed by an outlet (to which a household electrical appliance isconnected) in which the smart tap is installed and a communicationfunction with an external device.

In the smart tap, power (consumption) measured with the measurementfunction is transmitted to a display or the like with the communicationfunction, and, in the display, the power consumption from the smart tapis displayed, thereby realizing “visualization” of power consumption ofeach household electrical appliance.

However, installing smart taps in all the outlets in a household is noteasy in terms of costs.

In addition, a household electrical appliance fixed in a house, such asa so-called built-in air conditioner may be directly connected to apower line without using outlets in some cases, and thus it is difficultto use the smart tap for such a household electrical appliance.

Therefore, a technique called NILM (Non-Intrusive Load Monitoring) inwhich, for example, in a household or the like, from information oncurrent measured in a distribution board (power distribution board),power consumption or the like of each household electrical appliance inthe household connected ahead thereof has attracted attention.

In the NILM, for example, using current measured in a location, powerconsumption of each household electrical appliance (load) connectedahead therefrom is obtained without individual measurement.

For example, PTL 1 discloses an NILM technique in which active power andreactive power are calculated from current and voltage measured in alocation, and an electrical appliance is identified by clusteringrespective variation amounts.

In the technique disclosed in PTL 1, since variations in active powerand reactive power when a household electrical appliance is turned onand off are used, variation points of the active power and reactivepower are detected. For this reason, if detection of variation pointsfails, it is difficult to accurately identify the household electricalappliance.

Further, in recent household electrical appliances, it is difficult torepresent operating states as two states of ON and OFF, and thus it isdifficult to accurately identify household electrical appliances merelyby using variations in active power and reactive power in an ON stateand OFF state.

Therefore, for example, PTLs 2 and 3 disclose an NILM technique in whichLMC (Large Margin Classifier) such as SVM (Support Vector Machine) isused as an identification model (discriminative model, Classification)of household electrical appliances.

However, in the NILM using the identification model, unlike a generativemodel such as an HMM (Hidden Markov Model), existing learning data isprepared for each household electrical appliance, and learning of anidentification model using the learning data is required to be completedin advance.

For this reason, in the NILM using an identification model, it isdifficult to handle a household electrical appliance where learning ofan identification model is not performed using known learning data.

Therefore, for example, NPLs 1 and 2 disclose an NILM technique in whichan HMM which is a generative model is used instead of an identificationmodel of which learning is required using known learning data inadvance.

CITATION LIST Patent Literature

-   PTL 1: Specification of U.S. Pat. No. 4,858,141-   PTL 2: Japanese Unexamined Patent Application Publication No.    2001-330630-   PTL 3: International Publication brochure WO01/077696

Non Patent Literature

-   NPL 1: Bons M., Deville Y., Schang D. 1994. Non-intrusive electrical    load monitoring using Hidden Markov Models. Third international    Energy Efficiency and DSM Conference, October 31, Vancouver,    Canada., p. 7-   NPL 2: Hisahide NAKAMURA, Koichi ITO, Tatsuya SUZUKI, “Load    Monitoring System of Electric Appliances Based on Hidden Markov    Model”, IEEJ Transactions B, Vol. 126, No. 12, pp. 12231229, 2006

SUMMARY OF INVENTION Technical Problem

However, in the NILM using a simple HMM, if the number of householdelectrical appliances increases, the number of states of the HMM becomesenormous, and thus implementation thereof is difficult.

Specifically, for example, in a case where an operating state of eachhousehold electrical appliance is two states of ON and OFF, the numberof states of the HMM necessary to represent (a combination of) operationstates of M household electrical appliances is 2M, and the number oftransition probabilities of states is (2M)² which is a square of thenumber of the states.

Therefore, even if the number M of household electrical appliances in ahousehold is, for example, 20, although it cannot be said that there aremany household electrical appliances recently, the number of states ofthe HMM is 2²⁰=1,048,576, and the number of transition probabilities is1,099,511,627,776 corresponding to the square thereof, which is moreenormous in a tera-order.

At present, there is a demand for proposal of an NILM technique capableof easily and accurately obtaining power consumption or the like of ahousehold electrical appliance of which operating states are not onlytwo states of ON and OFF, that is, each electrical appliance such as ahousehold electrical appliance (variable load household electricalappliance), for example, an air conditioner of which power (current)consumption varies depending on modes, settings, or the like.

The present technology has been made in consideration of thesecircumstances and enables power consumption of each electrical applianceto be easily and accurately obtained.

Solution to Problem

According to an aspect of the present disclosure, there is provided amethod for estimating current consumption of an electrical device,including: obtaining data representing a sum of electrical signals oftwo or more electrical devices, the two or more electrical devicesincluding a first electrical device; processing the data with aFactorial Hidden Markov Model (FHMM) to produce an estimate of anelectrical signal of the first electrical device; and outputting theestimate of the electrical signal of the first electrical device,wherein the FHMM has a factor corresponding to the first electricaldevice, the factor having three or more states.

In some embodiments, the three or more states of the factor correspondto three or more respective electrical signals of the first electricaldevice in three or more respective operating states of the firstelectrical device.

In some embodiments, the method further includes: restricting the FHMMsuch that a number of factors of the FHMM which undergo state transitionat a same time point is less than a threshold number.

According to another aspect of the present disclosure, there is provideda monitoring apparatus, including: a data acquisition unit for obtainingdata representing a sum of electrical signals of two or more electricaldevices, the two or more electrical devices including a first electricaldevice; a state estimation unit for processing the data with a FactorialHidden Markov Model (FHMM) to produce an estimate of an operating stateof the first electrical device, the FHMM having a factor correspondingto the first electrical device, the factor having three or more states;and a data output unit for outputting an estimate of an electricalsignal of the first electrical device, the estimate of the electricalsignal being based at least in part on the estimate of the operatingstate of the first electrical device.

According to another aspect of the present disclosure, there is provideda monitoring apparatus, including: a data acquisition unit for obtainingdata representing a sum of electrical signals of two or more electricaldevices, the two or more electrical devices including a first electricaldevice; a state estimation unit for processing the data with a FactorialHidden Markov Model (FHMM) to produce an estimate of an operating stateof the first electrical device, the FHMM having a factor correspondingto the first electrical device, the factor having three or more states;a model learning unit for updating one or more parameters of the FHMM,wherein updating the one or more parameters of the FHMM comprisesperforming restricted waveform separation learning; and a data outputunit for outputting an estimate of an electrical signal of the firstelectrical device, the estimate of the electrical signal being based atleast in part on the estimate of the operating state of the firstelectrical device.

Advantageous Effects of Invention

According to the aspect of the present technology, it is possible toeasily and accurately obtain power consumption of each of electricalappliances.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an outline of an embodiment of amonitoring system to which a data processing apparatus of the presenttechnology is applied.

FIG. 2 is a diagram illustrating an outline of waveform separationlearning performed in household electrical appliance separation.

FIG. 3 is a block diagram illustrating a configuration example of thefirst embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 4 is a diagram illustrating an FHMM.

FIG. 5 is a diagram illustrating an outline of the formulation of thehousehold electrical appliance separation using the FHMM.

FIG. 6 is a flowchart illustrating a process of learning (learningprocess) of the FHMM according to an EM algorithm, performed by themonitoring system.

FIG. 7 is a flowchart illustrating a process of the E step performed instep S13 by the monitoring system.

FIG. 8 is a diagram illustrating relationships between the forwardprobability ALPHA_(t,z) and the backward probability BETA_(t,z) of theFHMM, and forward probability ALPHA_(t,i) and the backward probabilityBETA_(t,j) of the HMM.

FIG. 9 is a flowchart illustrating a process of the M step performed instep S14 by the monitoring system.

FIG. 10 is a flowchart illustrating an information presenting process ofpresenting information on a household electrical appliance #m, performedby the monitoring system.

FIG. 11 is a diagram illustrating a display example of the powerconsumption U^((m)) performed in the information presenting process.

FIG. 12 is a block diagram illustrating a configuration example of thesecond embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 13 is a flowchart illustrating a process of the E step performed instep S13 by the monitoring system.

FIG. 14 is a flowchart illustrating a process of the M step performed instep S14 by the monitoring system.

FIG. 15 is a block diagram illustrating a configuration example of thethird embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 16 is a diagram illustrating a method of obtaining the forwardprobability ALPHA_(t,p) by applying a particle filter to a combination zof states of the FHMM.

FIG. 17 is a diagram illustrating a method of obtaining the backwardprobability BETA_(t,p) by applying a particle filter to a combination zof states of the FHMM.

FIG. 18 is a diagram illustrating a method of obtaining the posteriorprobability GAMMA_(t,p) by applying a particle filter to a combination zof states of the FHMM.

FIG. 19 is a flowchart illustrating a process of the E step performed instep S13 by the monitoring system.

FIG. 20 is a flowchart illustrating a process of the E step performed instep S13 by the monitoring system.

FIG. 21 is a block diagram illustrating a configuration example of thefourth embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 22 is a flowchart illustrating a process of the M step of step S14performed by the monitoring system imposing a load restriction.

FIG. 23 is a diagram illustrating the load restriction.

FIG. 24 is a diagram illustrating a base waveform restriction.

FIG. 25 is a flowchart illustrating a process of the M step of step S14performed by the monitoring system imposing the base waveformrestriction.

FIG. 26 is a block diagram illustrating a configuration example of thefifth embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 27 is a diagram illustrating an outline of talker separation by themonitoring system which performs learning of the FHMM.

FIG. 28 is a block diagram illustrating a configuration example of thesixth embodiment of the monitoring system to which the presenttechnology is applied.

FIG. 29 is a flowchart illustrating a process of model learning(learning process) performed by the monitoring system.

FIG. 30 is a block diagram illustrating a configuration example of anembodiment of a computer to which the present technology is applied.

DESCRIPTION OF EMBODIMENTS

<Outline of Present Technology>

FIG. 1 is a diagram illustrating an outline of an embodiment of amonitoring system to which a data processing apparatus of the presenttechnology is applied. In some embodiments, a monitoring system may bereferred to as a smart meter.

In each household, power provided from an electric power company isdrawn to a distribution board (power distribution board) and is suppliedto electrical appliances such as household electrical appliances(connected to outlets) in the household.

The monitoring system to which the present technology is appliedmeasures a sum total of current which is consumed by one or morehousehold electrical appliances in the household in a location such asthe distribution board, that is, a source which supplies power to thehousehold, and performs household electrical appliance separation wherepower (current) consumed by individual household electrical appliancesuch as, for example, an air conditioner or a vacuum cleaner in thehousehold, from a series of the sum total of current (currentwaveforms).

In addition, as input data which is input to the monitoring system, inaddition to the sum total of current itself consumed by each householdelectrical appliance, sum total data regarding the sum total of currentconsumed by each household electrical appliance may be employed.

As the sum total data, a sum total of values which can be added may beemployed. Specifically, as the sum total data, in addition to the sumtotal of current itself consumed by each household electrical appliance,for example, a sum total of power consumed by each household electricalappliance, or a sum total of frequency components obtained by performingFFT (Fast Fourier Transform) for waveforms of current consumed by eachhousehold electrical appliance, may be employed.

In addition, in the household electrical appliance separation,information regarding current consumed by each household electricalappliance can be separated from the sum total data in addition to powerconsumed by each household electrical appliance. Specifically, in thehousehold electrical appliance separation, current consumed by eachhousehold electrical appliance or a frequency component thereof can beseparated from the sum total data.

In the following description, as the sum total data, for example, a sumtotal of current consumed by each household electrical appliance isemployed, and, for example, a waveform of current consumed by eachhousehold electrical appliance is separated from waveforms of the sumtotal of current which is the sum total data.

FIG. 2 is a diagram illustrating an outline of the waveform separationlearning performing the household electrical appliance separation.

In the waveform separation learning, a current waveform Y_(t) which issum total data at the time point t is set as an addition value (sumtotal) of a current waveform W^((m)) of current consumed by eachhousehold electrical appliance #m, and the current waveform W^((m))consumed by each household electrical appliance #m is obtained from thecurrent waveform Y_(t).

In FIG. 2, there are five household electrical appliances #1 to #5 inthe household, and, of the five household electrical appliances #1 to#5, the household electrical appliances #1, #2, #4 and #5 are in an ONstate (state where power is consumed), and the household electricalappliance #3 is in an OFF state (state where power is not consumed).

For this reason, in FIG. 2, the current waveform Y_(t) as the sum totaldata becomes an addition value (sum total) of current consumption W⁽¹⁾,W⁽²⁾, W⁽⁴⁾ and W⁽⁵⁾ of the respective household electrical appliances#1, #2, #4 and #5.

First Embodiment of Monitoring System to which Present Technology isApplied

FIG. 3 is a block diagram illustrating a configuration example of thefirst embodiment of the monitoring system to which the presenttechnology is applied.

In FIG. 3, the monitoring system includes a data acquisition unit 11, astate estimation unit 12, a model storage unit 13, a model learning unit14, a label acquisition unit 15, and a data output unit 16.

The data acquisition unit 11 acquires a time series of current waveformsY (current time series) as sum total data, and a time series of voltagewaveforms (voltage time series) V corresponding to the current waveformsY, so as to be supplied to the state estimation unit 12, the modellearning unit 14, and the data output unit 16.

In other words, the data acquisition unit 11 is constituted by ameasurement device (sensor) which measures, for example, current andvoltage.

The data acquisition unit 11 measures the current waveform Y as a sumtotal of current consumed by each household electrical appliance inwhich the monitoring system is installed in the household, for example,in a distribution board or the like and measures the correspondingvoltage waveform V, so as to be supplied to the state estimation unit12, the model learning unit 14, and the data output unit 16.

The state estimation unit 12 performs state estimation for estimating anoperating state of each household electrical appliance by using thecurrent waveform Y from the data acquisition unit 11, and overall models(model parameters thereof)

φ

which are stored in the model storage unit 13 and are models of overallhousehold electrical appliances in which the monitoring system isinstalled in the household. In addition, the state estimation unit 12supplies an operating state

Γ

of each household electrical appliance which is an estimation result ofthe state estimation, to the model learning unit 14, the labelacquisition unit 15, and the data output unit 16.

In other words, in FIG. 3, the state estimation unit 12 has anevaluation portion 21 and an estimation portion 22.

The evaluation portion 21 obtains an evaluation value E where thecurrent waveform Y supplied (to the state estimation unit 12) from thedata acquisition unit 11 is observed in each combination of states of aplurality of household electrical appliance models #1 to #M forming theoverall models

φ

stored in the model storage unit 13, so as to be supplied to theestimation portion 22.

The estimation portion 22 estimates a state of each of a plurality ofhousehold electrical appliances #1 to #M forming the overall models

φ

stored in the model storage unit 13, that is, an operating state

Γ

of a household electrical appliance indicated by the householdelectrical appliance model #m (a household electrical appliance modeledby the household electrical appliance model #m) by using the evaluationvalue E supplied from the evaluation portion 21, so as to be supplied tothe model learning unit 14, the label acquisition unit 15, and the dataoutput unit 16.

The model storage unit 13 stores overall models (model parameters)

φ

which are a plurality of overall models.

The overall models

φ

include household electrical appliance models #1 to #M which are Mmodels (representing current consumption) of a plurality of householdelectrical appliances.

The parameters

φ

of the overall models include a current waveform parameter indicatingcurrent consumption for each operating state of a household electricalappliance indicated by the household electrical appliance model #m.

The parameters

φ

of the overall models may include, for example, a state variationparameter indicating transition (variation) of an operating state of thehousehold electrical appliance indicated by the household electricalappliance model #m, an initial state parameter indicating an initialstate of an operating state of the household electrical applianceindicated by the household electrical appliance model #m, and a varianceparameter regarding a variance of observed values of the currentwaveform Y which is observed (generated) in the overall models.

The model parameters

φ

of the overall models stored in the model storage unit 13 are referredto by the evaluation portion 21 and the estimation portion 22 of thestate estimation unit 12, the label acquisition unit 15, and the dataoutput unit 16, and are updated by a waveform separation learningportion 31, a variance learning portion 32, and a state variationlearning portion 33 of the model learning unit 14, described later.

The model learning unit 14 performs model learning for updating themodel parameters

φ

of the overall models stored in the model storage unit 13, using thecurrent waveform Y supplied from the data acquisition unit 11 and theestimation result (the operating state of each household electricalappliance)

Γ

of the state estimation supplied from (the estimation portion 22 of) thestate estimation unit 12.

In other words, in FIG. 3, the model learning unit 14 includes thewaveform separation learning portion 31, the variance learning portion32, and the state variation learning portion 33.

The waveform separation learning portion 31 performs waveform separationlearning for obtaining (updating) a current waveform parameter which isthe model parameter

φ

by using the current waveform Y supplied (to the model learning unit 14)from the data acquisition unit 11 and the operating state

Γ

of each household electrical appliance supplied from (the estimationportion 22 of) the state estimation unit 12, and updates the currentwaveform parameter stored in the model storage unit 13 to a currentwaveform parameter obtained by the waveform separation learning.

The variance learning portion 32 performs variance learning forobtaining (updating) a variance parameter which is the model parameter

φ

by using the current waveform Y supplied (to the model learning unit 14)from the data acquisition unit 11 and the operating state

Γ

of each household electrical appliance supplied from (the estimationportion 22 of) the state estimation unit 12, and updates the varianceparameter stored in the model storage unit 13 to a variance parameterobtained by the variance learning.

The state variation learning portion 33 performs state variationlearning for obtaining (updating) an initial state parameter and a statevariation parameter which are the model parameters

φ

by using the operating state

Γ

of each household electrical appliance supplied from (the estimationportion 22 of) the state estimation unit 12, and updates the initialstate parameter and the state variation parameter stored in the modelstorage unit 13 to an initial state parameter and a state variationparameter obtained by the variance learning.

The label acquisition unit 15 acquires a household electrical appliancelabel L^((m)) for identifying a household electrical appliance indicatedby each household electrical appliance model #m by using the operatingstate

Γ

of each household electrical appliance supplied from (the estimationportion 22 of) the state estimation unit 12, the overall models

φstored in the model storage unit 13, and the power consumption U^((m))of a household electrical appliance of each household electricalappliance model #m, which is obtained by the data output unit 16, asnecessary, so as to be supplied to the data output unit 16.

The data output unit 16 obtains power consumption U^((m)) of a householdelectrical appliance indicated by each household electrical appliancemodel #m by using the voltage waveform V supplied from the dataacquisition unit 11, the operating state

Γφ

of each household electrical appliance supplied from (the estimationportion 22 of) the state estimation unit 12, and the overall modelsstored in the model storage unit 13, so as to be displayed on a display(not shown) and be presented to a user.

In addition, in the data output unit 16, the power consumption U^((m))of a household electrical appliance indicated by each householdelectrical appliance model #m may be presented to a user along with thehousehold electrical appliance label L^((m)) supplied from the labelacquisition unit 15.

In the monitoring system configured in the above-described way, as theoverall models stored in the model storage unit 13, for example, an FHMM(Factorial Hidden Markov Model) may be employed.

FIG. 4 is a diagram illustrating the FHMM.

In other words, A of FIG. 4 shows a graphical model of a normal HMM, andB of FIG. 4 shows a graphical model of the FHMM.

In the normal HMM, at the time point t, a single observation value Y_(t)is observed in a single state S_(t) which is at the time point t.

On the other hand, in the FHMM, at the time point t, a singleobservation value Y_(t) is observed in a combination of a plurality ofstates S⁽¹⁾ _(t), S⁽²⁾ _(t), . . . , and S^((M)) _(t) which are at thetime point t.

The FHMM is a probability generation model proposed by Zoubin Ghahramaniet al., and details thereof are disclosed in, for example, ZoubinGhahramani, and Michael I. Jordan, Factorial Hidden Markov Models',Machine Learning Volume 29, Issue 2-3, November/December 1997(hereinafter, also referred to as a document A).

FIG. 5 is a diagram illustrating an outline of the formulation of thehousehold electrical appliance separation using the FHMM.

Here, the FHMM includes a plurality of HMMs. Each HMM included in theFHMM is also referred to as a factor, and an m-th factor is indicated bya factor #m.

In the FHMM, a combination of a plurality of states S⁽¹⁾ _(t), toS^((M)) _(t) which are at the time point t is a combination of states ofthe factor #m (a set of a state of the factor #1, a state of the factor#2, . . . , and a state of the factor #M).

FIG. 5 shows the FHMM where the number M of the factors is three.

In the household electrical appliance separation, for example, a singlefactor corresponds to a single household electrical appliance (a singlefactor is correlated with a single household electrical appliance). InFIG. 5, the factor #m corresponds to the household electrical appliance#m.

In the FHMM, the number of states forming a factor is arbitrary for eachfactor, and, in FIG. 5, the number of states of each of the threefactors #1, #2 and #3 is four.

In FIG. 5, at the time point t=t0, the factor #1 is in a state #14(indicated by the thick line circle) among four states #11, #12, #13 and#14, and the factor #2 is in a state #21 (indicated by the thick linecircle) among four states #21, #22, #23 and #24. In addition, at thetime point t=t0, the factor #3 is in a state #33 (indicated by the thickline circle) among four states #31, #32, #33 and #34.

In the household electrical appliance separation, a state of a factor #mcorresponds to an operating state of the household electrical appliance#m corresponding to the factor #m.

For example, in the factor #1 corresponding to the household electricalappliance #1, the state #11 corresponds to an OFF state of the householdelectrical appliance #1, and the state #14 corresponds to an ON state ina so-called normal mode of the household electrical appliance #1. Inaddition, in the factor #1 corresponding to the household electricalappliance #1, the state #12 corresponds to an ON state in a so-calledsleep mode of the household electrical appliance #1, and the state #13corresponds to an ON state in a so-called power saving mode of thehousehold electrical appliance #1.

In the FHMM, in a state #mi of the factor #m, a unique waveform W^((m))_(#mi) which is a unique waveform for each state of the factor isobserved (generated).

In FIG. 5, in the factor #1, a unique waveform W⁽¹⁾ _(#14) is observedin the state #14 which is at the time point t=t0, and, in the factor #2,a unique waveform W⁽²⁾ _(#21) is observed in the state #21 which in atthe time point t=t0. Further, in the factor #3, a unique waveform W⁽³⁾_(#33) is observed in the state #33 which in at the time point t=t0.

In the FHMM, a synthesis waveform obtained by synthesizing uniquewaveforms observed in states of the respective factors is generated asan observation value which is observed in the FHMM.

Here, as the synthesis of unique waveforms, for example, a sum total(addition) of the unique waveforms may be employed. In addition, as thesynthesis of unique waveforms, for example, weight addition of theunique waveforms or logical sum (a case where a value of uniquewaveforms is 0 and 1) of the unique waveforms may be employed, and, inthe household electrical appliance separation, a sum total of uniquewaveforms is employed.

In the learning of the FHMM, model parameters of the FHMM are obtained(updated) such that current waveforms Y_(t0), Y_(t0+1), . . . areobserved which are sum total data at the respective time points t= . . ., t0, t1, . . . in the FHMM.

In a case of employing the above-described FHMM as the overall models

φ

stored in the model storage unit 13 (FIG. 3), a household electricalappliance model #m forming the overall models

φ

corresponds to the factor #m.

In addition, as the number M of factors of the FHMM, a value larger thanthe maximum number of household electrical appliances expected asexisting in a household by a predetermined amount which is a margin isemployed.

In addition, the FHMM as the overall models

φ

employs an FHMM where each factor has three or more states.

This is because, in a case where a factor has only two state, only twostates of, for example, an OFF state and an ON state are represented asoperating states of a household electrical appliance corresponding tothe factor, and it is difficult to obtain accurate power consumptionwith respect to a household electrical appliance (hereinafter, alsoreferred to as a variable load household electrical appliance) such asan air conditioner of which power (current) consumption varies dependingon modes or settings.

That is to say, if the FHMM as the overall models

φ

employs an FHMM where each factor has three or more states is employed,it is possible to obtain power consumption or the like with reference tothe variable load household electrical appliance.

In a case where the FHMM is employed as overall models

φ,

a simultaneous distribution P({S_(t),Y_(t)}) of a series of the currentwaveform Y_(t) observed in the FHMM and a combination S_(t) of a stateS^((m)) _(t) of the factor #m is calculated using Math (1) assuming theMarkov property.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack & \; \\{{P\left( \left\{ {S_{t},Y_{t}} \right\} \right)} = {{P\left( S_{1} \right)}{P\left( {Y_{1}S_{1}} \right)}{\prod\limits_{t = 2}^{1}\; {{P\left( {S_{t}S_{t - 1}} \right)}{P\left( {Y_{t}S_{t}} \right)}}}}} & (1)\end{matrix}$

Here, the simultaneous distance P({S_(t)Y_(t)}) indicates theprobability that the current waveform Y_(t) is observed in a combination(a combination of states of M factors) S_(t) of a state S^((m)) _(t) ofthe factor #m at the time point t.

P(S₁) indicates the initial state probability which is in a combinationS₁ of a state S^((m)) ₁ of the factor #m is in at the initial time pointt=1.

P(S_(t)|S_(t−1)) indicates the transition probability that a factor isin a combination S_(t−1) of states at the time point t−1 and transitionsto a combination S_(t) of states at the time point t.

P(Y_(t)|S_(t)) indicates the observation probability that the currentwaveform Y_(t) is observed in the combination S_(t) of states at thetime point t.

The combination S_(t) of states at the time point t is a combination ofstates S⁽¹⁾ _(t), S⁽²⁾ _(t), . . . , and S^((M)) _(t) at the time pointt of M factors #1 to #M, and is represented by the Math S_(t)={S⁽¹⁾_(t), S⁽²⁾ _(t), . . . , and S^((M)) _(t)}.

In addition, an operating state of the household electrical appliance #mis assumed to vary independently from another household electricalappliance #m′, and a state S^((m)) _(t) of the factor #m transitionsindependently from a state S^((m′)) _(t) of another factor #m′.

Further, the number K^((m)) of states of an HMM which is the factor #mof the FHMM may employ the number independent from the number K^((m′))of states of an HMM which is another factor #m′. However, here, forsimplicity of description, the numbers K⁽¹⁾ to K^((M)) of the factors #1to #M are the same number K as indicated by the Math K⁽¹⁾=K⁽²⁾= . . .=K^((M))=K.

In the FHMM, the initial state probability P(S₁), the transitionprobability P(S_(t)|S_(t−1)), and the observation probabilityP(Y_(t)|S_(t)) which are necessary to calculate the simultaneousdistance P(S_(t), Y_(t)) in Math (1) may be calculated as follows.

That is to say, the initial state probability P(S₁) may be calculatedaccording to Math (2).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack & \; \\{{P\left( S_{1} \right)} = {\prod\limits_{m = 1}^{M}\; {P\left( S_{1}^{(m)} \right)}}} & (2)\end{matrix}$

Here, P(S^((m)) ₁) indicates the initial state probability that thestate S^((m)) ₁ of the factor #m is a state (initial state) which is atthe initial time point t=1.

The initial state probability P(S^((m)) ₁) is, for example, a columnvector of K rows (a matrix of K rows and one column) which has aninitial state probability in the k-th (where k=1, 2, . . . , and K)state of the factor #m as a k-th row component.

The transition probability P(S_(t)|S_(t−1)) may be calculated accordingto Math (3).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 3} \right\rbrack & \; \\{{P\left( {S_{t}S_{t - 1}} \right)} = {\prod\limits_{m = 1}^{M}\; {P\left( {S_{1}^{(m)}S_{t - 1}^{(m)}} \right)}}} & (3)\end{matrix}$

Here, P(S^((m)) ₁|S^((m)) _(t−1)) indicates the transition probabilitythat the factor #m is in a state S^((m)) _(t−1) at the time point t−1and transitions to a state S^((m)) _(t) at the time point t.

The transition probability P(S^((m)) ₁|S^((m)) _(t−1)) is, for example,a matrix (square matrix) of K rows and K columns which has a transitionprobability that the factor #m transitions from the k-th state #k to thek′-th (where k′=1, 2, . . . , and K) state #k′ as a k-th row and k′-thcolumn component.

The observation probability P(Y_(t)|S_(t)) may be calculated accordingto Math (4).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 4} \right\rbrack & \; \\{{P\left( {Y_{t}S_{t}} \right)} = {{C}^{{- 1}/2}\left( {2\; \pi} \right)^{{- 0}/2}\exp {\left\{ {{- \frac{1}{2}}\left( {Y_{t} - \mu_{t}} \right)^{\prime}0^{- 1}\left( {Y_{t} - \mu_{t}} \right)} \right\}.}}} & (4)\end{matrix}$

Here, the dash (′) indicates transposition, and the superscript −1indicates an inverse number (inverse matrix). Further, |C| indicates anabsolute value (determinant) (determinant calculation) of C.

Further, D indicates a dimension of the observation value Y_(t).

For example, the data acquisition unit 11 in FIG. 3 samples a currentcorresponding to one cycle (1/50 seconds or 1/60 seconds) with apredetermined sampling interval using zero crossing timing when avoltage is changed from a negative value to a positive value as timingwhen a phase of a current is 0, and outputs a column vector which hasthe sampled values as components, as the current waveform Y_(t)corresponding to one time point.

If the number of samplings where the data acquisition unit 11 samples acurrent corresponding to one cycle is D, the current waveform Y_(t) is acolumn vector of D rows.

According to the observation probability P(Y_(t)|S_(t)) of Math (4), theobservation value Y_(t) follows the normal distribution where a variance(covariance matrix) is C when an average value (average vector) is

μ_(t).

The average value

μ_(t)

is, for example, a column vector of D rows which is the same as thecurrent waveform Y_(t), and the variance C is, for example, a matrix (amatrix of which diagonal components are a variance) of D rows and Dcolumns.

The average value

μ_(t)

is represented by Math (5.1) using the unique waveform W^((m)) describedwith reference to FIG. 5.

$\begin{matrix}{\mu_{t} = {\sum\limits_{m = 1}^{M}\; {W^{(m)}S_{t}^{*{(m)}}}}} & \left\lbrack {{Math}.\mspace{14mu} 5.1} \right\rbrack\end{matrix}$

. . . (5.1)

[Math.5.2]

P(T _(t) |S _(t))=|C|^(−1/2)(2π)^(−□/2)Exp{−½(□−μ)C(|−μ)+λ[St≠0]}  (5.2)

Observation Probability P(Y_(t)|S_(t)) may also be calculated accordingto Math (5.2), where

[S_(t)≠0]

is a number of non-zero states (states with non-zero data output) in allfactors, and lambda is a positive penalty to observation error. Thisadditional term will control to minimize the number of non-zero states.

Here, if a unique waveform of the state #k of the factor #m is indicatedby unique waveform W^((m)) _(k), a unique waveform W^((m)) _(k) of thestate #k of the factor #m is, for example, a column vector of D rowswhich is the same as the current waveform Y_(t).

In addition, the unique waveform W^((m)) is a collection of the uniquewaveforms W^((m)) ₁, W^((m)) ₂, . . . , and W^((m)) _(K) of therespective states #1, #2#, . . . , and #K of the factor #m, and is amatrix of D rows and K columns which has a column vector which is theunique waveform W^((m)) _(k) of the state #k of the factor #m as thek-th component.

In addition, S*^((m)) _(t) indicates a state of the factor #m which isat the time point t, and, hereinafter, S*^((m)) _(t) is also referred toas a present state of the factor #m at the time point t. The presentstate S*^((m)) _(t) of the factor #m at the time point t is, forexample, as shown in Math (6), a column vector of K rows where acomponent of only one row of K rows is 0, and components of the otherrows are 0.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 6} \right\rbrack & \; \\{S_{t}^{*{(m)}} = \begin{pmatrix}0 \\\vdots \\1 \\\vdots \\0\end{pmatrix}} & (6)\end{matrix}$

In a case where a state of the factor #m at the time point t is thestate #k, in a column vector S*^((m)) _(t) of K rows which is thepresent state S*^((m)) _(t) at the time point t, only the k-th rowcomponent is 1, and the other components are 0.

According to Math (5.1), a sum total of the unique waveform W^((m)) _(k)of the state #k of each factor #m at the time point t is obtained as theaverage value

μ_(t)

of the current waveform Y_(t) at the time point t.

The model parameters

φ

of the FHMM are the initial state probability P(S^((m)) ₁) of Math (2),the transition probability P(S^((m)) ₁|S^((m)) _(t−1)) of Math (3), thevariance C of the Math (4), and the unique waveform W^((m)) (=W^((m)) ₁,W^((m)) ₂, . . . , and W^((m)) _(K)) of Math (5.1), and the modelparameters

φof the FHMM are obtained in the model learning unit 14 of FIG. 3.

In other words, the waveform separation learning portion 31 obtains theunique waveform W^((m)) as a current waveform parameter through thewaveform separation learning. In the variance learning portion 32, thevariance C is obtained as a variance parameter through the variancelearning. The state variation learning portion 33 obtains the initialstate probability P(S^((m)) ₁) and the transition probability P(S^((m))₁|S^((m)) _(t−1)) as an initial state probability and a state variationparameter through the station variation learning.

Here, as described above, for example, even if operating states of eachhousehold electrical appliance are two states of ON and OFF, in a caseof representing (a combination of) operating states of twenty householdelectrical appliances by using a normal HMM, the number of states ofHMMs is 2²⁰=1,048,576, and the number of transition probabilities is1,099,511,627,776 which is the square thereof.

On the other hand, according to the FHMM, M household electricalappliances of which operating states are only two states of ON and OFFcan be represented by M factors each factor of which has two states.Therefore, since, in each factor, the number of states is two, and thenumber of transition probabilities is four which is the square thereof,in a case of representing operating states of household electricalappliances (factors) of M=20 using the FHMM, the number (total number)of states of the FHMM is

40=2×20

which is small, and the number of transition probabilities is

80=4×20

which is also small.

The learning of the FHMM, that is, update of the initial stateprobability P(S^((m)) ₁), the transition probability P(S^((m))_(t)|S^((m)) _(t−1)), the variance C, and the unique waveform W^((m))which are the model parameters

φ

of the FHMM, may be performed, for example, according to an EM(Expectation-Maximization) algorithm as disclosed in the document A.

In the learning of the FHMM using the EM algorithm, a process of the Estep and a process of M step are alternately repeated in order tomaximize an expected value

Q(φ^(new)|φ)

of a conditional complete data log likelihood of Math (7).

[Math.7]

Q(φ^(new)|φ)E{log P({S _(t) ,Y _(t)}|φ^(new))|φ,{S_(t) ,Y _(t)}}.  (7)

Here, the expected value

Q(φ^(new)|φ)

of the conditional complete data log likelihood means an expected valueof the log likelihood

log(P({S_(t),Y_(t)}|φ^(new)))

that the complete data {S_(t),Y_(t)} is observed under new modelparameters

φ^(new)

in a case where the complete data {S_(t),Y_(t)} is observed under themodel parameters

φ.

In a process of the E step of the EM algorithm, (a value correspondingto) an expected value

Q(φ^(new)|φ)

of the conditional complete data log likelihood of Math (7) is obtained,in a process of the M step of the EM algorithm, new model parameters

φ^(new)

for further increasing the expected value

Q(φ^(new)|φ)

obtained through the process of the E step are obtained, and the modelparameters

φ

are updated to the new model parameters

φ^(new)

(for further increasing the expected value

Q(φ^(new)|φ)).

FIG. 6 is a flowchart illustrating a process of learning (learningprocess) of the FHMM according to the EM algorithm, performed by themonitoring system (FIG. 3).

In step S11, the model learning unit 14 initializes the initial stateprobability P(S^((m)) ₁), the transition probability P(S^((m))_(t)|S^((m)) _(t−1)), the variance C, and the unique waveform W^((m))which are the model parameters

φ

of the FHMM stored in the model storage unit 13, and the processproceeds to step S12.

Here, a k-th row component of the column vector of K rows which is theinitial state probability P(S^((m)) ₁), that is, a k-th initial stateprobability

π^((m)) _(k)of the factor #m is initialized to, for example, 1/K.

An i-th row and j-th column component (where i and j=1, 2, . . . , andK) of the matrix of K rows and K columns which is the transitionprobability P (S^((m)) _(t)|S^((m)) _(t−1)), that is, the transitionprobability P^((m)) _(i,j) that the factor #m transitions from the i-thstate #i to the j-th state #j is initialized using random numbers so asto satisfy a Math P^((m)) _(i,1)+P^((m)) _(i,2)+ . . . +P^((m))_(i,K)=1.

The matrix of D rows and D columns which is the variance C isinitialized to, for example, a diagonal matrix of D rows and D columnswhere diagonal components are set using random numbers and the othercomponents are 0.

A k-th column vector of the matrix of D rows and K columns which is theunique waveform W^((m)), that is, each component of the column vector ofD rows which is the unique waveform W^((m)) _(k) of the state #k of thefactor #m is initialized using, for example, random numbers.

In step S12, the data acquisition unit 11 acquires current waveformscorresponding to a predetermined time T and supplies current waveformsat the respective time points t=1, 2, . . . , and T to the stateestimation unit 12 and the model learning unit 14 as measurementwaveforms Y₁, Y₂, . . . , and Y_(T), and the process proceeds to stepS13.

Here, the data acquisition unit 11 also acquires voltage waveforms alongwith the current waveforms at the time points t=1, 2, . . . , and T. Thedata acquisition unit 11 supplies the voltage waveforms at the timepoints t=1, 2, . . . , and T to the data output unit 16.

In the data output unit 16, the voltage waveforms from the dataacquisition unit 11 are used to calculate power consumption.

In step S13, the state estimation unit 12 performs the process of the Estep using the measurement waveforms Y₁ to Y_(T) from the dataacquisition unit 11, and the process proceeds to step S14.

In other words, in step S13, the state estimation unit 12 performs stateestimation for obtaining a state probability or the like which is ineach state of each factor #m of the FHMM stored in the model storageunit 13 by using the measurement waveforms Y₁ to Y_(T) from the dataacquisition unit 11, and supplies an estimation result of the stateestimation to the model learning unit 14 and the data output unit 16.

Here, as described with reference to FIG. 5, in the household electricalappliance separation, a state of the factor #m corresponds to anoperating state of the household electrical appliance #m correspondingto the factor #m. Therefore, the state probability in the state #k ofthe factor #m of the FHMM indicates an extent that an operating state ofthe household electrical appliance #m is the state #k, and thus it canbe said that the state estimation for obtaining such a state probabilityis to obtain (estimate) an operating state of a household electricalappliance.

In step S14, the model learning unit 14 performs the process of the Mstep using the measurement waveforms Y₁ to Y_(T) from the dataacquisition unit 11 and the estimation result of the state estimationfrom the state estimation unit 12, and the process proceeds to step S15.

In other words, in step S14, the model learning unit 14 performslearning of the FHMM of which each factor has three or more state,stored in the model storage unit 13 by using the measurement waveformsY₁ to Y_(T) from the data acquisition unit 11 and the estimation resultof the state estimation from the state estimation unit 12, therebyupdating the initial state probability

π^((m)) _(k),

the transition probability P^((m)) _(i,j), and the variance C, and theunique waveform W^((m)), which are the model parameters

φ

of the FHMM stored in the model storage unit 13.

In step S15, the model learning unit 14 determines whether or not aconvergence condition of the model parameters

φ

is satisfied.

Here, as the convergence condition of the model parameters

φ,

for example, a condition in which the processes of the E step and the Mstep are repeatedly performed a predetermined number of times set inadvance, or a condition that a variation amount in the likelihood thatthe measurement waveforms Y₁ to Y_(T) are observed before update of themodel parameters

φ

and after update thereof in the FHMM is in a threshold value set inadvance, may be employed.

In step S15, if it is determined that the convergence condition of themodel parameters

φ

is not satisfied, the process returns to step S13, and, thereafter, thesame process is repeatedly performed.

Further, if it is determined that the convergence condition of the modelparameters

φ

is satisfied in step S15, the learning process finishes.

FIG. 7 is a flowchart illustrating a process of the E step performed instep S13 of FIG. 6 by the monitoring system of FIG. 3.

In step S21, the evaluation portion 21 obtains the observationprobability P(Y_(t)|S_(t)) of Math (4) as an evaluation value E withrespect to each combination S_(t) of states at the time points t={1, 2,. . . , and T} by using the variance C of the FHMM as the overall models

φstored in the model storage unit 13, the unique waveform W^((m)), andthe measurement waveforms Y_(t)={Y₁, Y₂, . . . , and Y_(T)} from thedata acquisition unit 11, so as to be supplied to the estimation portion22, and the process proceeds to step S22.

In step S22, the estimation portion 22 obtains the forward probability

α_(t,z)

(ALPHA_(t,z)) that the measurement waveforms Y₁, Y₂, . . . , and Y_(t)are measured and which is in a combination (a combination of a state ofthe factor #1, a state of the factor #2, . . . , and a state of thefactor #M at the time point t) z of states at the time point t, by usingthe observation probability P(Y_(t)|S_(t)) from the evaluation portion21, and the transition probability P^((m)) _(i,j) (and the initial stateprobability

π^((m)))

of p^((m)) the FHMM as the overall models

φ

stored in the model storage unit 13, and the process proceeds to stepS23.

Here, a method of obtaining a forward probability of an HMM is disclosedin, for example, p. 336 of C. M. Bishop “Pattern Recognition And MachineLearning (II): Statistical Inference based on Bayes Theory”, SpringerJapan, 2008 (hereinafter, also referred to as a document B).

The forward probability

α_(t,z)

may be obtained according to, for example, the recurrence formula

α_(t,z)=Σα_(t−1,w) P(z|w)P(Y _(t) |z)

using the forward probability

α_(t−1,w)

before one time point.

In the recurrence formula

α_(t,z)=Σα_(t−1,w) P(z|w)P(Y _(t) |z),Σ

indicates summation taken by changing w to all the combinations ofstates of the FHMM.

In addition, in the recurrence formula

α_(t,z)=Σα_(t−1,w) P(z|w)P(Y _(t) |z),

w indicates a combination of states at the time point t−1 before onetime point. P(z|w) indicates the transition probability that a factor isin the combination w of states at the time point t−1 and transitions tothe combination z of states at the time point t. P(Y_(t)|z) indicatesthe observation probability that the measurement waveform Y_(t) isobserved in the combination z of states at the time point t.

In addition, as an initial value of the forward probability

α_(t,z),

that is, the forward probability

α_(1,z)

at the time point t=1, a product of the initial state probability

π^((m)) _(k)

of the state #k of each factor #m forming the combination z of states.

In step S23, the estimation portion 22 obtains the backward probability

β_(t,z)

(BETA_(t,z)) which is in the combination z of states at the time point tand, thereafter, that the measurement waveforms Y_(t), Y_(t+1), . . . ,and Y_(T) are observed, by using the observation probabilityP(Y_(t)|S_(t)) from the evaluation portion 21, and the transitionprobability P^((m)) _(i,j) of the FHMM as the overall models

φ

stored in the model storage unit 13, and the process proceeds to stepS24.

Here, a method of obtaining the backward probability of an HMM isdisclosed, for example, in page 336 of the above-described document B.

The backward probability

β_(t,z)

may be obtained according to the recurrence formula

β_(t,z) =ΣP(Y _(t) |z)P(z|w)β_(t+1,w)

using the forward probability

β_(t+1,w)

after one time point.

In the recurrence formula

β_(t,z) =ΣP(Y _(t) |z)P(z|w)β_(t+1,w),Σ

indicates summation taken by changing w to all the combinations ofstates of the FHMM.

Further, in the recurrence formula

β_(t,z) =ΣP(Y _(t) |z)P(z|w)β_(t+1,w) w

indicates a combination of states at the time point t+1 after one timepoint. P(z|w) indicates the transition probability that a factor is inthe combination z of states at the time point t and transitions to thecombination w of states at the time point t+1. P(Y_(t)|z) indicates theobservation probability that the measurement waveform Y_(t) is observedin the combination z of states at the time point t.

In addition, as an initial value of the backward probability

β_(t,z),that is, the backward probabilityβ_(t,z)at the time point t=T, 1 is employed.

In step S24, the estimation portion 22 obtains a posterior probability

γ_(t,z)

which is in the combination z of states at the time point t in the FHMMas the overall models

φ

according to Math (8) by using the forward probability

α_(t,z)

and the backward probability

β_(t,z),

and process proceeds to step S25.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 8} \right\rbrack & \; \\{\gamma_{t,z} = \frac{\alpha_{t,z}\beta_{t,z}}{\sum\limits_{w \in S_{t}}\; {\alpha_{t,w}\beta_{t,w}}}} & (8)\end{matrix}$

Here,

Σ

of the denominator of the right side of Math (8) indicates summationtaken by changing w to all the combinations S_(t) of states which can betaken at the time point t.

According to Math (8), the posterior probability

γ_(t,z)

is obtained by normalizing a product

α_(t,z)β_(t,z)

of the forward probability

α_(t,z)

and the backward probability

β_(t,z)

with a sum total

Σα_(t,w)β_(t,z)

of the product

α_(t,w)β_(t,w)

for combinations

wεS_(t)

of states which can be taken by the FHMM.

In step S25, the estimation portion 22 obtains the posterior probability<S^((m)) _(t)> that the factor #m is in the state S^((m)) _(t) at thetime point t and the posterior probability <S^((m)) _(t)S^((n)) _(t)′>that the factor #m is in the state S^((m)) _(t) and another factor #n isin the state S^((n)) _(t) at the time point t, by using the posteriorprobability

γ_(t,z),

and the process proceeds to step S26.

Here, the posterior probability <S^((m)) _(t)> may be obtained accordingto Math (9).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 9} \right\rbrack & \; \\{{\langle S_{t}^{(m)}\rangle} = {\sum\limits_{z \in {S_{t}^{(n)}{({n \neq m})}}}\; \gamma_{t,z}}} & (9)\end{matrix}$

According to Math (9), the posterior probability <S^((m)) _(t)> that thefactor #m is in the state S^((m)) _(t) at the time point t is obtainedby marginalizing the posterior probability

γ_(t,z)

in the combination z of state at the time point t with respect to thecombination z of states which does not include states of the factor #m.

In addition, the posterior probability <S^((m)) _(t)> is, for example, acolumn vector of K rows which has a state probability (posteriorprobability) that the factor #m is in the k-th state of K states thereofat the time point t as a k-th row component.

The posterior probability <S^((m)) _(t)S^((n)) _(t)′> may be obtainedaccording to Math (10).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 10} \right\rbrack & \; \\{{\langle{S_{t}^{(m)}S_{t}^{{(n)}^{\prime}}}\rangle} = {\sum\limits_{z \in {{S_{t}^{(r)}{({r \neq {m\; \alpha \; r} \neq n})}}.}}\; \gamma_{t,z}}} & (10)\end{matrix}$

According to Math (10), the posterior probability <S^((m)) _(t)S^((n))_(t)′> that the factor #m is in the state S^((m)) _(t) and anotherfactor #n is in the state S^((m)) _(t) at the time point t is obtainedby marginalizing the posterior probability

γ_(t,z)

in the combination z of states at the time point t with respect to thecombination z of state which does not include any of states of thefactor #m and states of the factor #n.

In addition, the posterior probability <S^((m)) _(t)S^((n)) _(t)′> is,for example, a matrix of K rows and K columns which has a stateprobability (posterior probability) in the state #k of the factor #m andthe state #k′ of another factor #n as a k-th row and k′-th columncomponent.

In step S26, the estimation portion 22 obtains the posterior probability<S^((m)) _(t−1)S^((m)) _(t)′> that the factor #m is in the state S^((m))_(t−1) at the time point t−1 and is in the state S^((m)) _(t) at the nextime point t, by using the forward probability

α_(t,z),

the backward probability

β_(t,z),

the transition probability P(z|w), and the observation probabilityP(Y_(t)|S_(t)) from the evaluation portion 21.

In addition, the estimation portion 22 supplies the posteriorprobabilities <S^((m)) _(t)>, <S^((m)) _(t)S^((n)) _(t)′> and <S^((m))_(t−1)S^((m)) _(t)′> to the model learning unit 14, the labelacquisition unit 15, and the data output unit 16 as an estimation resultof the state estimation, and returns from the process of the E step.

Here, the posterior probability <S^((m)) _(t−1)S^((m)) _(t)′> may beobtained according to Math (11).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 11} \right\rbrack & \; \\{{\langle{S_{t - 1}^{(m)}S_{t}^{{(m)}^{\prime}}}\rangle} = \frac{\sum\limits_{{w = S_{t - 1}^{(n)}},{z \in {S_{t}^{(1)}{({{n \neq m}{r \neq m}})}}}}\; {\alpha_{{t - 1},w}{P\left( {zw} \right)}{P\left( {Y_{t}z} \right)}\beta_{t,z}}}{\sum\limits_{{w = S_{t - 1}},{z \in S_{t}}}\; {\alpha_{{t - 1},w}{P\left( {zw} \right)}{P\left( {Y_{t}z} \right)}\beta_{t,z}}}} & (11)\end{matrix}$

In calculation of the posterior probability <S^((m)) _(t−1)S^((m))_(t)′> of Math (11), the transition probability P(z|w) that a factortransitions from the combination w of states to the combination z ofstates is obtained as a product

P⁽¹⁾ _(i(1),j(1))×P⁽²⁾ _(i(2),j(2))× . . . ×P^((M)) _(i(M),j(M))

of the transition probability P⁽¹⁾ _(i(1),j(1)) from the state #i(1) ofthe factor #1 forming the combination w of states to the state #j(1) ofthe factor #1 forming the combination z of states, the transitionprobability P⁽²⁾ _(i(2),j(2)) from the state #i(2) of the factor #2forming the combination w of states to the state #j(2) of the factor #2forming the combination z of states, . . . , and the transitionprobability P^((M)) _(i(M),j(M)) from the state #i(M) of the factor #Mforming the combination w of states to the state #j(M) of the factor #Mforming the combination z of states, according to Math (3).

In addition, the posterior probability <S^((m)) _(t−1)S^((m)) _(t)′> is,for example, a matrix of K rows and K columns which has a stateprobability (posterior probability) that the factor #m is in the state#i at the time point t−1, and is in the state j at the next time point tas an i-th row and j-th column component.

FIG. 8 is a diagram illustrating a relationship between the forwardprobability

α_(t,z)

and the backward probability

β_(t,z)

of the FHMM, and the forward probability

α_(t,i)

(ALPHA_(t,i)) and the backward probability

β_(t,j)

(BETA_(t,j) of a (normal) HMM.

In relation to the FHMM, an HMM equivalent to the FHMM can beconfigured.

An HMM equivalent to a certain FHMM has states corresponding to thecombination z of states of respective factors of the FHMM.

In addition, the forward probability

α_(t,z)

and the backward probability

β_(t,z)

of the FHMM conform to the forward probability

α_(t,i)

and the backward probability

β_(t,i)

of the HMM equivalent to the FHMM.

A of FIG. 8 shows an FHMM including the factors #1 and #2 each of whichhas two states #1 and #2.

In the FHMM of A of FIG. 8, as a combination z=[k,k′] of the state #k ofthe factor #1 and the state #k′ of the factor #2, there are fourcombinations, a combination [1,1] of the state #1 of the factor #1 andthe state #1 of the factor #2, a combination [1,2] of the state #1 ofthe factor #1 and the state #2 of the factor #2, a combination [2,1] ofthe state #2 of the factor #1 and the state #1 of the factor #2, and acombination [2,2] of the state #2 of the factor #1 and the state #2 ofthe factor #2.

B of FIG. 8 shows an HMM equivalent to the FHMM of A of FIG. 8.

The HMM of B of FIG. 8 has four states #(1,1), #(1,2) #(2,1) #(2,2)which respectively correspond to the four combinations [1,1], [1,2],[2,1], and [2,2] of the states of the FHMM of A of FIG. 8.

In addition, the forward probability

α_(t,z)={α_(t,[1,1]),α_(t,[1,2]),α_(t,[2,1]),α_(t,[2,2])}

of the FHMM of A of FIG. 8 conforms to the forward probability

α_(t,i)={α_(t,[1,1]),α_(t,[1,2]),α_(t,[2,1]),α_(t,[2,2])}

of the HMM of B of FIG. 8.

Similarly, the backward probability

β_(t,z)={β_(t,[1,1]),β_(t,[1,2]),β_(t,[2,1]),β_(t,[2,2])}

of the FHMM of A of FIG. 8 conforms the forward probability

β_(t,i)={β_(t,[1,1]),β_(t,[1,2]),β_(t,[2,1]),β_(t,[2,2])}

of the HMM of B of FIG. 8.

For example, the denominator of the right side of the above-describedMath (8), that is, the sum total

Σα_(t,w)β_(t,w)

of the product

α_(t,w)β_(t,w)

for combinations

wεS_(t)

of states which can be taken by the FHMM is indicated by the Math

Σα_(t,w)β_(t,w)=α_(t,[1,1])β_(t,[1,1])+α_(t,[1,2])β_(t,[1,2])+α_(t,[2,1])β_(t,[2,1])+β_(t,[2,2])β_(t,[2,2])

for the FHMM of A of FIG. 8.

FIG. 9 is a flowchart illustrating a process of the M step performed instep S14 of FIG. 6 by the monitoring system of FIG. 3.

In step S31, the waveform separation learning portion 31 performs thewaveform separation learning by using the measurement waveform Y_(t)from the data acquisition unit 11, and the posterior probabilities<S^((m)) _(t)> and <S^((m)) _(t)S^((m)) _(t)′> from the estimationportion 22, so as to obtain an update value W^((m)new) of the uniquewaveform W^((m)), and updates the unique waveform W^((m)) stored in themodel storage unit 13 to the update value W^((m)new), and the processproceeds to step S32.

In other words, the waveform separation learning portion 31 calculatesMath (12) as the waveform separation learning, thereby obtaining theupdate value W^((m)new) of the unique waveform W^((m)).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 12} \right\rbrack & \; \\{W^{new} = {\left( {\sum\limits_{t = 1}^{T}\; {Y_{t}{\langle S_{t}^{\prime}\rangle}}} \right)\left( {\sum\limits_{t = 1}^{T}{\langle{S_{t}S_{t}^{\prime}}\rangle}} \right)^{*}}} & (12)\end{matrix}$

Here, W^(new) is a matrix of D rows and

K×M

columns in which the update value W^((m)new) of the unique waveformW^((m)) of the factor #m which is a matrix of D rows and K columns isarranged in order of the factor (index thereof) #m from the left to theright. A column vector of (m−1)K+k columns of the unique waveform(update value thereof) W^(new) which is a matrix of D rows and

K×M columns is a unique waveform W^((m)) _(k) (update value thereof) ofthe state #k of the factor #m.

<S_(t)′> is a row vector of

K×M

columns obtained by transposing a column vector of

K×M

rows in which the posterior probability <S^((m)) _(t)> which is a columnvector of K rows is arranged in order of the factor #m from the top tothe bottom. A ((m−1)K+k)-th column component of the posteriorprobability <S_(t)′> which is the row vector of

K×M

columns is a state probability which is in the state #k of the factor #mat the time point t.

<S_(t)S_(t)′> is a matrix of

K×M

rows and

K×M

columns in which the posterior probability <S^((m)) _(t)S^((n)) _(t)′>which is a matrix of K rows and K columns is arranged in order of thefactor #m from the top to the bottom and is arranged in order of thefactor #n from the left to the right. A ((m−1)K+k)-th row and((n−1)K+k′)-th column component of the posterior probability<S_(t)S_(t)′> which is a matrix of

K×M

rows and

K×M

columns is a state probability which is in is in the state #k of thefactor #m and is in the state #k′ of another factor #n at the time pointt.

The superior asterisk (*) indicates an inverse matrix or apseudo-inverse matrix.

According to the waveform separation learning for calculating Math (12),the measurement waveform Y_(t) is separated into a unique waveformW^((m)) such that an error between the measurement waveform Y_(t) andthe average value

μ_(t) =ΣW ^((m)) S* ^((m)) _(t)),

of Math (5.1) becomes as small as possible.

In step S32, the variance learning portion 32 performs the variancelearning by using the measurement waveform Y_(t) from the dataacquisition unit 11, the posterior probability <S^((m)) _(t)> from theestimation portion 22, and the unique waveform W^((m)) stored in themodel storage unit 13, so as to obtain an update value C^(new) of thevariance C, and updates the variance C stored in the model storage unit13, and the process proceeds to step S33.

In other words, the variance learning portion 32 calculates Math (13) asthe variance learning, thereby obtaining the update value C^(new) of thevariance C.

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 13} \right\rbrack & \; \\{C^{new} = {{\frac{1}{T}{\sum\limits_{t = 1}^{T}{Y_{t}Y_{t}^{\prime}}}} - {\frac{1}{T}{\sum\limits_{m = 1}^{M}{W^{(m)}{\langle S_{t}^{(m)}\rangle}Y_{t}^{\prime}}}}}} & (13)\end{matrix}$

In step S33, the state variation learning portion 33 performs thestation variation learning by using the posterior probabilities <S^((m))_(t)> and <S^((m)) _(t)S^((m)) _(t)′> from the estimation portion 22 soas to obtain an update value P^((m)) _(i,j) ^(new) of the transitionprobability P^((m)) _(i,j) and

an update valueπ^((m)new)of the initial state probabilityπ^((m)),and updates the transition probability P^((m)) _(i,j) and the initialstate probabilityπ^((m))stored in the model storage unit 13 to the update value P^((m)) _(i,j)^(new) of the update valueπ^((m)new),and the process returns from the process of the M step.

In other words, the state variation learning portion 33 calculates Math(14) and (15) as the state variation learning, thereby obtaining theupdate value P^((m)) _(i,j) ^(new) of the transition probability P^((m))_(i,j) and the update value

π^((m)new)

of the initial state probability

π^((m)).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 14} \right\rbrack & \; \\{P_{i,j}^{{(m)}{new}} = \frac{\sum\limits_{t = 2}^{T}{\langle{S_{t - 1}^{(m)},S_{t - j}^{(m)}}\rangle}}{\sum\limits_{t = 2}^{T}{\langle S_{{t - 1},i}^{(m)}\rangle}}} & (14) \\\left\lbrack {{Math}.\mspace{11mu} 15} \right\rbrack & \; \\{\pi^{{(m)}{new}} = {\langle S_{1}^{(m)}\rangle}} & (15)\end{matrix}$

Here, <S^((m)) _(t−1,i)S^((m)) _(t,j)> is an i-th row and j-th columncomponent of the posterior probability <S^((m)) _(t−1)S^((m)) _(t)′>which is a matrix of K rows and K columns, and indicates a stateprobability that the factor #m is in the state #i at the time point t−1and is in the state #j at the next time point t.

<S^((m)) _(t−1,i)> is an i-th row component of the posterior probability<S^((m)) _(t−1)> which is a column vector of K rows, and indicates astate probability that the factor #m is in the state #i at the timepoint t−1.

π^((m))(π^((m)new))

is a column vector of K rows which has (the update value

π^((m)) _(k) ^(new) of)

the initial state probability

π^((m)) _(k)

of the state #k of the factor #m as a k-th row component.

FIG. 10 is a flowchart illustrating an information presenting process ofpresenting information on a household electrical appliance #m, performedby the monitoring system (FIG. 3).

In step S41, the data output unit 16 obtains power consumption U^((m))of each factor #m by using the voltage waveform (a voltage waveformcorresponding to the measurement waveform Y_(t) which is a currentwaveform) V_(t) from the data acquisition unit 11, the posteriorprobability <S^((m)) _(t)> which is the estimation result of the stateestimation from the state estimation unit 12, and the unique waveformW^((m)) stored in the model storage unit 13, and the process proceeds tostep S42.

Here, the data output unit 16 obtains the power consumption U_((m)) ofthe household electrical appliance #m corresponding to the factor #m atthe time point t by using the voltage waveform V_(t) at the time point tand the current consumption A_(t) of the household electrical appliance#m corresponding to the factor #m at the time point t.

In the data output unit 16, the current consumption A_(t) of thehousehold electrical appliance #m corresponding to the factor #m at thetime point t may be obtained as follows.

That is to say, for example, in the factor #m, the data output unit 16obtains the unique waveform W^((m)) of the state #k where the posteriorprobability <S^((m)) _(t)> is the maximum, as the current consumptionA_(t) of the household electrical appliance #m corresponding to thefactor #m at the time point t.

In addition, the data output unit 16 obtains weighted addition values ofthe unique waveforms W^((m)) ₁, W^((m)) ₂, . . . , and W^((m)) _(K) ofeach state of the factor #m using the state probability of each state ofthe factor #m at the time point t which is a component of the posteriorprobability <S^((m)) _(t)> which is a column vector of K columns as aweight, as the current consumption A_(t) of the household electricalappliance #m corresponding to the factor #m at the time point t.

In addition, if the learning of the FHMM progresses, and the factor #mbecomes a household electrical appliance model which appropriatelyrepresents the household electrical appliance #m, in relation to thestate probability of each state of the factor #m at the time point t,the state probability of a state corresponding to an operating state ofthe household electrical appliance #m at the time point t becomes nearly1, and the state probabilities of the remaining (K−1) states becomenearly 0.

As a result, in the factor #m, the unique waveform W^((m)) of the state#k where the posterior probability <S^((m)) _(t)> is the maximum issubstantially the same as the weighted addition values of the uniquewaveforms W^((m)) ₁, W^((m)) ₂, . . . , and W^((m)) _(K) of each stateof the factor #m using the state probability of each state of the factor#m at the time point t as a weight.

In step S42, the label acquisition unit 15 acquires a householdelectrical appliance label L^((m)) for identifying a householdelectrical appliance #m indicated by each household electrical appliancemodel #m, that is, a household electrical appliance #m corresponding toeach factor #m of the FHMM, so as to be supplied to the data output unit16, and, the process proceeds to step S43.

Here, the label acquisition unit 15 may display, for example, thecurrent consumption A_(t) or the power consumption U^((m)) of thehousehold electrical appliance #m corresponding to each factor #m,obtained in the data output unit 16, and a use time slot of thehousehold electrical appliance #m recognized from the power consumptionU^((m)), receive the name of a household electrical appliance matchingthe current consumption A_(t) or the power consumption U^((m)), and theuse time slot from a user, and acquire the name of the householdelectrical appliance input by the user as the household electricalappliance label L^((m)).

Further, the label acquisition unit 15 may prepare for a database inwhich, with respect to various household electrical appliances,attributes such as power consumption thereof, a current waveform(current consumption), and a use time slot are registered in correlationwith names of the household electrical appliances in advance, andacquire the name of a household electrical appliance correlated with thecurrent consumption A_(t) or the power consumption U^((m)) of thehousehold electrical appliance #m corresponding to each factor #m,obtained in the data output unit 16, and a use time slot of thehousehold electrical appliance #m recognized from the power consumptionU^((m)), as the household electrical appliance label L^((m)).

In addition, in the label acquisition unit 15, in relation to the factor#m corresponding to the household electrical appliance #m of which thehousehold electrical appliance label L^((m)) has already been acquiredand supplied to the data output unit 16, the process in step S42 may beskipped.

In step S43, the data output unit 16 displays the power consumptionU^((m)) of each factor #m on a display (not shown) along with thehousehold electrical appliance label L^((m)) of each factor #m so as tobe presented to a user, and the information presenting process finishes.

FIG. 11 is a diagram is a diagram illustrating a display example of thepower consumption U^((m)), performed in the information presentingprocess of FIG. 10.

The data output unit 16 displays, for example, as shown in FIG. 11, atime series of the power consumption U^((m)) of the household electricalappliance #m corresponding to each factor #m on a display (not shown)along with the household electrical appliance label L^((m)) such as thename of the household electrical appliance #m.

As described above, the monitoring system performs learning of the FHMMfor modeling an operating state of each household electrical applianceusing the FHMM of which each factor has three or more states, and thusit is possible to obtain accurate power consumption or the like withrespect to a variable load household electrical appliance such as an airconditioner of which power (current) consumption varies depending onmodes, settings, or the like.

In addition, since, in the monitoring system, a sum total of currentconsumed by each household electrical appliance in a household ismeasured in a location such as a distribution board, and thereby powerconsumption of each household electrical appliance in the household canbe obtained, it is possible to easily realize “visualization” of powerconsumption of each household electrical appliance in the household interms of both costs and efforts as compared with a case of installing asmart tap in each outlet.

In addition, by the “visualization” of power consumption of eachhousehold electrical appliance in a household, for example, it ispossible to raise the awareness of power saving in the household.

Further, power consumption of each household electrical appliance in ahousehold obtained by the monitoring system is collected by, forexample, a server, and, a use time slot of the household electricalappliance, and further a life pattern may be estimated from the powerconsumption of the household electrical appliances of each household andbe helpful in marketing and the like.

Second Embodiment of Monitoring System to which Present Technology isApplied

FIG. 12 is a block diagram illustrating a configuration example of thesecond embodiment of the monitoring system to which the presenttechnology is applied.

In addition, in the figure, portions corresponding to the case of FIG. 3are given the same reference numerals, and, hereinafter, descriptionthereof will be appropriately omitted.

The monitoring system of FIG. 12 is common to the monitoring system ofFIG. 3 in that the data acquisition unit 11 to the data output unit 16are provided.

However, the monitoring system of FIG. 12 is different from themonitoring system of FIG. 3 in that an individual variance learningportion 52 is provided instead of the variance learning portion 32 ofthe model learning unit 14.

In the variance learning portion 32 of FIG. 3, a single variance C isobtained for the FHMM as overall models in the variance learning, but,in the individual variance learning portion 52 of FIG. 12, an individualvariance

σ^((m))

is obtained for the FHMM as overall models in the variance learning foreach factor #m or for each state #k of each factor #m.

Here, in a case where the individual variance

σ^((m))

is an individual variance for each state #k of each factor #m, theindividual variance

σ^((m))

is, for example, a row vector of K columns which has a variance

σ^((m)) _(k)of the state #k of the factor #m as a k-th column component.

In addition, hereinafter, for simplicity of description, in a case wherethe individual variance

σ^((m))

is an individual variance for each factor #m, all the components

σ^((m)) ₁, σ^((m)) ₂, . . . , and σ^((m)) _(K)

of the row vector of K columns which is the individual variance

σ^((m))

are set to the same value (the same scalar value as a variance for thefactor #m, or a (covariance) matrix) common to the factor #m, andthereby the individual variance

σ^((m))

which is separate for each factor #m is treated to be the same as theindividual variance

σ^((m))

which is separate for each state #k of each factor #m.

The variance

σ^((m)) _(K)

of the state #k of the factor #m which is a k-th column component of therow vector of K columns which is the individual variance

σ^((m))

is a scalar value which is a variance, or a matrix of D rows and Dcolumns which is a covariance matrix.

In a case where the individual variance

σ^((m))

is obtained in the individual variance learning portion 52, theevaluation portion 21 obtains the observation probability P(Y_(t)|S_(t))according to Math (16) instead of Math (4) by using the individualvariance

σ^((m)).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 16} \right\rbrack & \; \\{P\left( {{Y_{t}\left. S_{t} \right)} = {{\Sigma_{t}}^{{- 1}/2}\left( {2\pi} \right)^{{- 0}/2}\exp {\left\{ {{- \frac{1}{2}}\left( {Y_{t} - \mu_{t}} \right)^{\prime}{\Sigma_{t}^{- 1}\left( {Y_{t} - \mu_{t}} \right)}} \right\}.}}} \right.} & (16)\end{matrix}$

Here,

Σt

of Math (16) may be obtained according to Math (17).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 17} \right\rbrack & \; \\{\Sigma_{t} = {\sum\limits_{m = 1}^{M}{\sigma^{(m)}S_{t}^{*{(m)}}}}} & (17)\end{matrix}$

S*^((m)) _(t) indicates a state of the factor #m at the time point t asshown in Math (6), and, is a column vector of K rows where a componentof only one row of K rows is 0, and components of the other componentsare 0.

In the individual variance learning portion 52, the individual variance

σ^((m))

may be obtained as follows.

That is to say, an expected value

<σ>

of the variance

σ

of the FHMM as the overall models

φ

for the measurement waveform Y_(t) which is actually measured (observed)is the same as an expected value <|Y_(t)−Ŷ_(t)|²> of the square error|Y_(t)−Ŷ_(t)|² of a current waveform which is the measurement waveformY_(t) and a current waveform which is a generation waveform Ŷ_(t)generated (observed) in the FHMM as the overall models

φ,

as indicated in Math (18).

[Math.18]

<σ>=

|Y _(t) −Ŷ _(t)|²

  (18)

The expected value

<σ>

of the variance

σ

is equivalent to a sum total

σ⁽¹⁾<S⁽¹⁾ _(t)>+σ⁽²⁾<S⁽²⁾ _(t)>+ . . . +σ^((M))<S^((M)) _(t)>

for the factor #m of an expected value

σ^((m))<S^((m)) _(t)>

of the individual variance

σ^((m))

which is separate for each state #k of each factor #m.

In addition, the expected value <|Y_(t)−Ŷ_(t)|²> of the square error|Y_(t)−Ŷ_(t)|² of the measurement waveform Y_(t) and the generationwaveform Ŷ_(t) is equivalent to the square error |Y_(t)−(W⁽¹⁾<S⁽¹⁾_(t)>+W⁽²⁾<S⁽²⁾ _(t)>+ . . . +W^((M))<S^((M)) _(t)>)|² of themeasurement waveform Y_(t) and a sum total W⁽¹⁾<S⁽¹⁾ _(t)>+W⁽²⁾<S⁽²⁾_(t)>+ . . . +W^((M))<S^((M)) _(t)> of an expected value W^((m))<S^((m))_(t)> of an individual waveform W^((m)) for the factor #m.

Therefore, Math (18) is equivalent to Math (19).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 19} \right\rbrack & \; \\{{\sum\limits_{m = 1}^{M}{\sigma^{(m)}{\langle S_{t}^{(m)}\rangle}}} = {{Y_{t} - {\sum\limits_{m = 1}^{M}{W^{(m)}{\langle S_{t}^{(m)}\rangle}}}}}^{2}} & (19)\end{matrix}$

The individual variance

σ^((m))=σ^((m)new)

satisfying Math (19) may be obtained according to Math (20) using arestricted quadratic programming with a restriction where the individualvariance

σ^((m))

is not negative.

$\begin{matrix}{\mspace{79mu} \left\lbrack {{Math}.\mspace{11mu} 20} \right\rbrack} & \; \\{\sigma^{{(m)}{new}} = {{\underset{\sigma^{(m)}}{argmin}{\sum\limits_{t = 1}^{T}{{{{{Y_{t} - {\sum\limits_{m = 1}^{M}{W^{(m)}{\langle S_{t}^{(m)}\rangle}}}}}^{2} - {\sum\limits_{m = 1}^{M}{\sigma^{(m)}{\langle S_{t}^{(m)}\rangle}}}}}^{2}\mspace{14mu} {subject}\mspace{14mu} 0}}} < \sigma^{(m)}}} & (20)\end{matrix}$

The individual variance learning portion 52 an update value

σ^((m)new)

of the individual variance

σ^((m))

according to Math (20), and updates the individual variance

σ^((m))

stored in the model storage unit 13 to the update value

σ^((m)new).

As described above, in the individual variance learning portion 52, in acase where the individual variance

σ^((m))

is obtained for each factor #m or for each state #k of each factor #m,representation performance where the FHMM as the overall models

φ

represents a household electrical appliance is improved as compared witha case of using a single variance C, accuracy of state estimation of theestimation portion 22 is also improved, and thus it is possible toobtain power consumption or the like more accurately.

Particularly, for example, with respect to a variable load householdelectrical appliance such as a vacuum cleaner of which currentconsumption varies depending on a sucking state, it is possible to powerconsumption more accurately by using the individual variance

σ^((m)).

The monitoring system of FIG. 12 performs the same processes (thelearning process of FIG. 6 or the information presenting process of FIG.10) as the monitoring system of FIG. 3 except that the individualvariance

σ^((m))

is used instead of the variance C.

FIG. 13 is a flowchart illustrating a process of the E step performed instep S13 of FIG. 6 by the monitoring system of FIG. 12.

In step S61, the evaluation portion 21 obtains the observationprobability P(Y_(t)|S_(t)) as an evaluation value E in each combinationS_(t) of states at the time points t={1, 2, . . . , and T} according toMath (16) and (17) (instead of Math (4)) by using the individualvariance

σ^((m))

of the FHMM as the overall models

φ

stored in the model storage unit 13, the unique waveform W^((m)), andthe measurement waveforms Y_(t)={Y₁, Y₂, . . . , and Y_(T)} from thedata acquisition unit 11, so as to be supplied to the estimation portion22, and the process proceeds to step S62.

Hereinafter, in steps S62 to S66, the same processes as in steps S22 toS26 of FIG. 7 are respectively performed.

FIG. 14 is a flowchart illustrating a process of the M step performed instep S14 of FIG. 6 by the monitoring system of FIG. 12.

In step S71, in the same manner as step S31 of FIG. 9, the waveformseparation learning portion 31 performs the waveform separation learningso as to obtain an update value W^((m)new) of the unique waveformW^((m)), and updates the unique waveform W^((m)) stored in the modelstorage unit 13 to the update value W^((m)new), and the process proceedsto step S72.

In step S72, the individual variance learning portion 52 performs thevariance learning according to Math (20) by using the measurementwaveform Y_(t) from the data acquisition unit 11, the posteriorprobability <S^((m)) _(t)> from the estimation portion 22, and theunique waveform W^((m)) stored in the model storage unit 13, so as toobtain an update value

σ^((m)new)

of the individual variance

σ^((m)).

In addition, the individual variance learning portion 52 updates theindividual variance

σ^((m))

stored in the model storage unit 13 to the update value

σ^((m)new)

of the individual variance

σ^((m)),

and the process proceeds to step S73 from step S72.

In step S73, in the same manner as step S33 of FIG. 9, the statevariation learning portion 33 performs the station variation learning soas to obtain an update value P^((m)) _(i,j) ^(new) of or the transitionprobability P^((m)) _(i,j) and an update value

π^((m)new)

of the initial state probability

π^((m)),

and updates the transition probability P^((m)) _(i,j) and the initialstate probability

π^((m))

stored in the model storage unit 13 to the update value P^((m)) _(i,j)^(new) of the update value

π^((m)new),

and the process returns from the process of the M step.

Third Embodiment of Monitoring System to which Present Technology isApplied

FIG. 15 is a block diagram illustrating a configuration example of thethird embodiment of the monitoring system to which the presenttechnology is applied.

In addition, in the figure, portions corresponding to the case of FIG. 3are given the same reference numerals, and, hereinafter, descriptionthereof will be appropriately omitted.

The monitoring system of FIG. 15 is common to the monitoring system ofFIG. 3 in that the data acquisition unit 11 to the data output unit 16are provided.

However, the monitoring system of FIG. 15 is different from themonitoring system of FIG. 3 in that an approximate estimation portion 42is provided instead of the estimation portion 22 of the state estimationunit 12.

The approximate estimation portion 42 obtains the posterior probability(state probability)<S^((m)) _(t)> under a state transition restrictionwhere the number of factors of which a state transitions for one timepoint is restricted.

Here, for example, the posterior probability <S^((m)) _(t)> is obtainedby marginalizing the posterior probability

γ_(t,z)

according to Math (9).

The posterior probability

γ_(t,z)

may be obtained in related to all the combinations z of states of therespective factors of the FHMM by using the forward probability

α_(t,z)

and the backward probability

β_(t,z)

according to Math (8).

However, the number of the combinations z of states of the respectivefactors of the FHMM is K^(M) and is thus increased in an exponentialorder if the number M of factors is increased.

Therefore, in a case where the FHMM has a number of factors, if theforward probability

α_(t,z),

the backward probability

β_(t,z),

and the posterior probability

γ_(t,z)

are strictly calculated for all the combinations z of states of therespective factors as disclosed in the document A, a calculation amountbecomes enormous.

Therefore, in relation to state estimation of the FHMM, for example, anapproximate estimation method such as Gibbs Sampling, CompletelyFactorized Variational Approximation, or Structured VariationalApproximation is proposed in the document A. However, in the approximateestimation method, there are cases where a calculation amount is stilllarge, or accuracy is considerably reduced due to approximation.

However, a probability that operating states of the overall householdelectrical appliances in a household are changed (varied) at each timeis considerably low except for an abnormal case such as power failure.

Therefore, the approximate estimation portion 42 obtains the posteriorprobability <S^((m)) _(t)> under a state transition restriction wherethe number of factors of which a state transitions for one time point.

The state transition restriction may employ a restriction where thenumber of factors of which a state transitions for one time point isrestricted to one, or several or less.

According to the state transition restriction, the number ofcombinations z of states which require strict calculation of theposterior probability

γ_(t,z),and, further the forward probabilityα_(t,z)and the backward probabilityβ_(t,z)used to obtain the posterior probability <S^((m)) _(t)> is greatlyreduced, and, the reduced combinations of states are combinations whichhave a considerably low occurrence probability, thereby notably reducinga calculation amount without greatly damaging accuracy of the posteriorprobability (state probability)<S^((m)) _(t)> or the like.

In the approximate estimation portion 42, as methods of obtaining theposterior probability <S^((m)) _(t)> under the state transitionrestriction for restricting the number of factors of which a statetransitions for one time point, there is a method of applying a particlefilter to a process of the E step, that is, applying a particle filterto a combination z of states of the FHMM.

Here, the particle filter is disclosed in, for example, page 364 of theabove-described document B.

In addition, in the present embodiment, the state transition restrictionemploys a restriction where the number of factors of which a statetransitions for one time point is restricted to, for example, one orless.

In a case of applying the particle filter to the combination z of statesof the FHMM, a p-th particle S_(p) of the particle filter represents acertain combination of states of the FHMM.

Here, the particle S_(p) is a combination of a state S⁽¹⁾ _(p) of thefactor #1, a state S⁽²⁾ _(p) of the factor #2, . . . , and a stateS^((M)) _(p) of the factor #M, represented by the particle S_(p), and isexpressed by the Math particle S_(p)={S(S⁽¹⁾ _(p), S⁽²⁾ _(p), . . . ,and S^((M)) _(p)}.

In addition, in a case of applying the particle filter to thecombination z of states of the FHMM, instead of the observationprobability P(Y_(t)|S_(p)) of Math (4) that the measurement waveformY_(t) is observed in a combination S_(t) of states, observationprobability P(Y_(t)|S_(p)) that the measurement waveform Y_(t) isobserved in the particle S_(p) (a combination of states representedthereby) is used.

The observation probability P(Y_(t)|S_(p)) may be calculated, forexample, according to Math (21).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 21} \right\rbrack & \; \\{P\left( {{Y_{t}\left. S_{p} \right)} = {{C}^{{- 1}/2}\left( {2\pi} \right)^{{- 0}/2}\exp \left\{ {{- \frac{1}{2}}\left( {Y_{t} - \mu_{p}} \right)^{\prime}{C^{- 1}\left( {Y_{t} - \mu_{p}} \right)}} \right\}}} \right.} & (21)\end{matrix}$

The observation probability P(Y_(t)|S_(t)) of Math (21) is differentfrom the observation probability P(Y_(t)|S_(p)) of Math (4) in that anaverage value (average vector) is not μ_(t) but μ_(p).

In the same manner as the average value

μ_(t),

the average value

μ_(p)

is, for example, a column vector of D rows which is the same as thecurrent waveform Y and is expressed by the Math (22) using the uniquewaveform W^((m)).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 22} \right\rbrack & \; \\{\mu_{p} = {\sum\limits_{m = 1}^{M}{W^{(m)}S_{p}^{*{(m)}}}}} & (22)\end{matrix}$

Here, S*^((m)) _(p) indicates a state of the factor #m of states forminga combination of the states which is the particle S_(p), and, ishereinafter also referred to as a state S*^((m)) _(p) of the factor #mof the particle S_(p). The state S*^((m)) _(p) of the factor #m of theparticle S_(p) is, for example, as shown in Math (23), a column vectorof K rows where a component of only one row of K rows is 0, andcomponents of the other rows are 0.

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 23} \right\rbrack & \; \\{S_{p}^{*{(m)}} = \begin{pmatrix}0 \\\vdots \\1 \\\vdots \\0\end{pmatrix}} & (23)\end{matrix}$

In a case where a state of the factor #m forming a combination of statesas the particle S_(p) is the state #k, in a column vector S*^((m)) _(p)of K rows which is the state S*^((m)) _(p) of the factor #m of theparticle S_(p), only the k-th row component is 1, and the othercomponents are 0.

According to Math (22), a sum total of the unique waveform W^((m)) _(k)of the state #k of each factor #m forming a combination of states as theparticle S_(p) is obtained as the average value

μ_(p)

of the current waveform Y_(t) at the time point t.

FIG. 16 is a diagram illustrating a method of obtaining the forwardprobability

α_(t,p)

(ALPHA_(t,p)) by applying the particle filter to the combination z ofstates of the FHMM.

In FIG. 16 (the same for FIGS. 17 and 18 described later), for example,the number of factors of the FHMM is four, and, each factor has twostates representing an ON state and an OFF state as operating states.

The evaluation portion 21 obtains the observation probabilityP(Y_(t)|S_(p)) of Math (21) in a combination of states as each particleS_(p)={S₁, S₂, . . . , and S_(R)} at the time points t={1, 2, . . . ,and T} by using the variance C of the FHMM as the overall models

φ

stored in the model storage unit 13, the unique waveform W^((m)), andthe measurement waveforms Y_(t)={Y_(t), Y₂, . . . , and Y_(T)} from thedata acquisition unit 11, so as to be supplied to the approximateestimation portion 42.

The approximate estimation portion 42 obtains the forward probability

α_(t,p)

that the measurement waveforms Y₁, Y₂, . . . , and Y_(t) are measuredand which is in a combination as the particle S_(p) at the time point tby using the observation probability P(Y_(t)|S_(p)) from the evaluationportion 21, and the transition probability P^((m)) _(i,j) (and theinitial state probability

π^((m)))

of the FHMM as the overall models

φ

stored in the model storage unit 13.

The forward probability

α_(t,p)

may be obtained according to, for example, the recurrence formula

α_(t,p)=Σα_(t−1,r) P(S _(p) |r)P(Y _(t) |S _(p))

using the forward probability

α_(t−1,r)

before one time point.

In the recurrence formula

α_(t,p)=Σα_(t−1,r) P(S _(p) |r)P(Y _(t) |S _(p)),

r indicates a combination r of states as a particle at the time pointt−1 which can transition to a combination of states as the particleS_(p) at the time point t under the state transition restriction wherethe number of factors of which a state transitions for one time point isrestricted to one state or less.

Therefore, a combination of states where a combination of states equalto a combination of states as the particle S_(p) is different from thecombination of states as the particle S_(p) only by one state can becomethe combination r of states.

In the recurrence formula

α_(t,p)=Σα_(t−1,r) P(S _(p) |r)P(Y _(t) |S _(p)),Σ

indicates summation taken for all the combinations r of states.

In addition, in the recurrence formula

α_(t,p)=Σα_(t−1,r) P(S _(p) |r)P(Y _(t) |S _(p)),P(S _(p) |r)

indicates the transition probability that a factor is in the combinationr of states as the particle S_(p) at the time point t−1 and transitionsto a combination of states as the particle S_(p) at the time point t,and may be obtained according to Math (3) by using the transitionprobability P^((m)) _(i,j) of the FHMM as the overall modelsφ.

In addition, as an initial value of the forward probability

α_(t,p),

for example, the forward probability

α_(1,z)

at the time point t=1, a product of the initial state probability

π^((m)) _(k)

of the state #k of each factor #m forming a combination of states whichis randomly selected as the particle S_(p) from all the combinations ofstates.

The approximate estimation portion 42 obtains the forward probability

α_(t,p)

with respect to not all the combinations of states which can be taken bythe FHMM but only a combination of states as the particle S_(p).

If the number of the particle S_(p) which exists at the time point t isR, R forward probabilities

α_(t,p)

are obtained.

If the forward probability

α_(t,p)

is obtained with respect to R particles S_(p)={S₁, S₂, . . . , andS_(R)} at the time point t, the approximate estimation portion 42samples particles from the R particles S_(p)={S₁, S₂, . . . , and S_(R)}on the basis of the forward probability

α_(t,p).

In other words, the approximate estimation portion 42 samplespredetermined P particles S_(p)={S₁, S₂, . . . , and S_(P)} indescending order of the forward probability

α_(t,p)

from the R particles S_(p)={S₁, S₂, . . . , and S_(R)}, and leaves onlythe P particles S_(p) as particles at the time point t.

In addition,

if P≧R,

all of the R particles S_(p) are sampled.

In addition, the number P of the particles S_(p) to be sampled is avalue smaller than the number of combinations of states which can betaken by the FHMM, and, is set, for example, based on calculation costsallowable for the monitoring system.

If P particles S_(p)={S₁, S₂, . . . , and S_(P)} at the time point t aresampled, the approximate estimation portion 42 predicts particles at thetime point t+1 after one time point in each of the P particlesS_(p)={S₁, S₂, . . . , and S_(P)} under the state transitionrestriction.

Under the state transition restriction where the number of factors ofwhich a state transitions for one time point is restricted to one stateor less, as a particle S_(q) at the time point t+1, a combination ofstates where a combination of states equal to a combination of states asthe particle S_(p) is different from the combination of states as theparticle S_(p) only by one state is predicted.

As described above, in a case where the number of factors of the FHMM isfour, and each factor has two states representing an ON state and an OFFstate as operating states, in relation to the particle S_(p) at the timepoint t, a total of five combinations with a combination of states equalto a combination of states as the particle S_(p) at the time point t andfour combinations which are different from the combination of states asthe particle S_(p) at the time point t only by one state, are predictedas particles S_(q) at the time point t+1.

The approximate estimation portion 42 predicts the particles S_(q)={S₁,S₂, . . . , S_(Q)} at the time point t+1, then obtains the forwardprobability with respect to the particles S_(q) at the time point t+1 inthe same manner as the particles S_(p) at the time point t, and,thereafter, obtains forward probabilities at the time points t=1, 2, . .. , and T in the same manner.

As described above, the approximate estimation portion 42 obtains theforward probability

α_(t,z)

of a combination z of states as a particle for each time point by usinga combination of states of each factor #m as a particle while repeatedlypredicting particles after one time point under the state transitionrestriction and sampling a predetermined number of particles on thebasis of the forward probability

α_(t,z).

FIG. 17 is a diagram illustrating a method of obtaining the backwardprobability

β_(t,p)

(BETA_(t,p)) by applying a particle filter to the combination z ofstates of the FHMM.

The approximate estimation portion 42 obtains the backward probability

β_(t,p)

which is in a combination a of states as the particle S_(p) at the timepoint t, and, thereafter, that the measurement waveforms Y_(t), T_(t+1),. . . , and Y_(T) are observed, by using the observation probabilityP(Y_(t)|S_(t)) from the evaluation portion 21, and the transitionprobability P^((m)) _(i,j) of the FHMM as the overall models

φ

stored in the model storage unit 13.

The backward probability

β_(t,p)

may be obtained according to the recurrence formula

β_(t,p) =ΣP(Y _(t) |S _(p))P(r|S _(p))β_(t+1,r)

using the forward probability

β_(t+1,r)

after one time point.

In the recurrence formula

β_(t,p) =ΣP(Y _(t) |S _(p))P(r|S _(p))β_(t+1,r),

r indicates a combination r of states as a particle at the time pointt+1 which can transition to a combination of states as the particleS_(p) at the time point t under the state transition restriction wherethe number of factors of which a state transitions for one time point isrestricted to one state or less.

Therefore, a combination of states where a combination of states equalto a combination of states as the particle S_(p) is different from thecombination of states as the particle S_(p) only by one state can becomethe combination r of states.

In the recurrence formula

β_(t,p) =ΣP(Y _(t) |S _(p))P(r|S _(p))β_(t+1,r),Σ

indicates summation taken for all the combinations r of states.

In the recurrence formula

β_(t,p) =ΣP(Y _(t) |S _(p))P(r|S _(p))β_(t+1,r),

P(r|S_(p)) indicates the transition probability that a factor is in thecombination r of states as the particle S_(p) at the time point t andtransitions to a combination of states as the particle S_(p) at the timepoint t+1, and may be obtained according to Math (3) by using thetransition probability P^((m)) _(i,j) of the FHMM as the overall models

φ.

In addition, an initial value

β_(T,p)

of the backward probability

β_(t,p)

employs 1.

In the same manner as the forward probability

α_(t,p),

the approximate estimation portion 42 obtains the backward probability

β_(t,p)

with respect to not all the combinations of states which can be taken bythe FHMM

but only a combination of states as the particle S_(p).

If the number of the particle S_(p) which exists at the time point t isR, R backward probabilities

β_(t,p)

are obtained.

If the backward probability β_(t,p)

is obtained with respect to R particles S_(p)={S₁, S₂, . . . , andS_(R)} at the time point t, the approximate estimation portion 42samples particles from the R particles S_(p)={S₁, S₂, . . . , and S_(R)}on the basis of the backward probability

β_(t,p).

In other words, in the same manner as a case of the forward probability

α_(t,p),

the approximate estimation portion 42 samples predetermined P particlesS_(p)={S₁, S₂, . . . , and S_(P)} in descending order of the backwardprobability

β_(t,p)

from the R particles S_(p)={S₁, S₂, . . . , and S_(R)}, and leaves onlythe P particles S_(p) as particles at the time point t.

If P particles S_(p)={S₁, S₂, . . . , and S_(P)} at the time point t aresampled, the approximate estimation portion 42 predicts particles at thetime point t−1 before one time point in each of the P particlesS_(p)={S₁, S₂, . . . , and S_(P)} under the state transitionrestriction.

Under the state transition restriction where the number of factors ofwhich a state transitions for one time point is restricted to one stateor less, as a particle S_(q) at the time point t−1, a combination ofstates where a combination of states equal to a combination of states asthe particle S_(p) is different from the combination of states as theparticle S_(p) at the time point t only by one state is predicted.

As described above, in a case where the number of factors of the FHMM isfour, and each factor has two states representing an ON state and an OFFstate as operating states, in relation to the particle S_(p) at the timepoint t, a total of five combinations with a combination of states equalto a combination of states as the particle S_(p) at the time point t andfour combinations which are different from the combination of states asthe particle S_(p) at the time point t only by one state, are predictedas particles S_(q) at the time point t−1.

The approximate estimation portion 42 predicts the particles S_(q)={S₁,S₂, . . . , and S_(Q)} at the time point t−1, then obtains the backwardprobability

β_(t+1,q)

in the same manner as the particles S_(p) at the time point t, and,thereafter, obtains forward probabilities at the time points t=T, T−1, .. . , and 1 in the same manner.

As described above, the approximate estimation portion 42 obtains thebackward probability

β_(t,z)

of a combination z of states as a particle for each time point by usinga combination of states of each factor #m as a particle while repeatedlypredicting particles before one time point under the state transitionrestriction and sampling a predetermined number of particles on thebasis of the backward probability

β_(t,z).

FIG. 18 is a diagram illustrating a method of obtaining the posteriorprobability

γ_(t,p)

(GAMMA_(t,p)) by applying a particle filter to the combination z ofstates of the FHMM.

In a case where a combination of states as the particle (hereinafter,also referred to as a forward particle) S_(p) of which the forwardprobability

α_(t,p)

has been obtained at each time point t and a combination of states asthe particle (hereinafter, referred to as a backward particle) S_(p′) ofwhich the backward probability

β_(t,p′)

has been obtained are the same combination z of states, the approximateestimation portion 42 obtains the posterior probability

γ_(t,z)

in the combination z of states at the time point t in the FHMM as theoverall models

φ

according to Math (23) by using the forward probability

α_(t,z)

and the backward probability

β_(t,z)

relation to the combination z of states.

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 24} \right\rbrack & \; \\{\gamma_{t,z} = \frac{\alpha_{t,z}\beta_{t,z}}{\sum\limits_{w \in {S_{p}\bigcap S_{p^{\prime}}}}{\alpha_{t,w}\beta_{t,w}}}} & (24)\end{matrix}$

Here,

Σ

of the denominator of the right side of Math (24) indicates summationtaken by changing w to all the combinations

S_(p)∩S_(p′)

of states which are commonly left in both the forward particle S_(p) andthe backward particle S_(p′) at the time point t.

In addition, both the forward probability

α_(t,z)

of the combination z of states which is not left as the forward particleS_(p) at the time point t and the backward probability

β_(t,z)

of the combination z of states which is not left as the backwardparticle S_(p′) at the time point t are, for example, 0.

Therefore, the posterior probability

γ_(t,z)

of combinations of states other than the combinations

S_(p)∩S_(p′)

of states which are commonly left in both the forward particle S_(p) andthe backward particle S_(p′) at the time point t is 0.

The monitoring system of FIG. 15 performs the same process as themonitoring system of FIG. 3 except that the posterior probability<S^((m)) _(t)> or the like is obtained under the state transitionrestriction by applying a particle filter to the combination z of statesof the FHMM.

FIGS. 19 and 20 are flowcharts illustrating a process of the E stepperformed in step S13 of FIG. 6 by the monitoring system of FIG. 15.

In addition, FIG. 20 is a flowchart subsequent to FIG. 19.

In step S81, the approximate estimation portion 42 initializes (avariable indicating) the time point t to 1 as an initial value for aforward probability, and the process proceeds to step S82.

In step S82, the approximate estimation portion 42 selects (samples) apredetermined number of combinations of states, for example, in random,from combinations of states which can be taken by the FHMM, as particlesS_(p). In addition, in step S82, all of the combinations of states whichcan be taken by the FHMM may be selected as the particles S_(p).

The approximate estimation portion 42 obtains an initial value

α_(1,p)

of the forward probability

α_(t,p)

with respect to the combinations of states as the particles S_(p), andthe process proceeds from step S82 to step S83.

In step S83, it is determined whether or not the time point t is thesame as the last time point (series length) T of the measurementwaveform Y_(t)={Y₁, Y₂, . . . , and Y_(T)}.

If it is determined that the time point t is not the same as the lasttime point T of a series of the measurement waveform Y_(t) in step S83,that is, the time point t is less than the time point T, the processproceeds to step S84 where the approximate estimation portion 42predicts a particle at the time point t+1 after one time point withrespect to the combinations of states as the particles S_(p) under thestate transition restriction which is a predetermined condition, and theprocess proceeds to step S85.

In step S85, the approximate estimation portion 42 increments the timepoint t by 1, and the process proceeds to step S86.

In step S86, the evaluation portion 21 obtains the observationprobability P(Y_(t)|S_(p)) of Math (21) with respect to the combinationsof states as the particles S_(p)={S₁, S₂, . . . , and S_(R)} at the timepoint t by using the variance C of the FHMM as the overall models storedin the model storage unit 13, the unique waveform W^((m)), and themeasurement waveform Y_(t)={Y₁, Y₂, . . . , and Y_(T)} from the dataacquisition unit 11. The evaluation portion 21 supplies the observationprobability P(Y_(t)|S_(p)) to the approximate estimation portion 42, andthe process proceeds from step S86 to step S87.

In step S87, the approximate estimation portion 42 measures themeasurement waveforms Y₁, Y₂, . . . , and Y_(t) by using the observationprobability P(Y_(t)|S_(p)) from the evaluation portion 21, and thetransition probability P^((m)) _(i,j) (and the initial stateprobability)

π^((m)))

of the FHMM as the overall models

φ

stored in the model storage unit 13, and obtains the forward probability

α_(t,p)

in a combination as the particle S_(p) at the time point t, and, theprocess proceeds to step S88.

In step S88, the approximate estimation portion 42 samples apredetermined number of particles in descending order of the forwardprobability

α_(t,p)

from the particles S_(p) so as to be left as particles at the time pointt on the basis of the forward probability

α_(t,p).

Thereafter, the process returns to step S83 from step S88, and,hereinafter, the same processes are repeatedly performed.

In addition, it is determined that the time point t is the same as thelast time point T of a series of the measurement waveform Y_(t) in stepS83, the process proceeds to step S91 of FIG. 20.

In step S91, the approximate estimation portion 42 initializes the timepoint t to T as an initial value for a backward probability, and theprocess proceeds to step S92.

In step S92, the approximate estimation portion 42 selects (samples) apredetermined number of combinations of states, for example, in random,from combinations of states which can be taken by the FHMM, as particlesS_(p). In addition, in step S92, all of the combinations of states whichcan be taken by the FHMM may be selected as the particles S_(p).

The approximate estimation portion 42 obtains an initial value

α_(T,p)

of the backward probability

β_(t,p)

with respect to the combinations of states as the particles S_(p), andthe process proceeds from step S92 to step S93.

In step S93, it is determined whether or not the time point t is thesame as the initial time point 1 of the measurement waveform Y_(t)={Y₁,Y₂, . . . , and Y_(T)}.

If it is determined that the time point t is not the same as the initialtime point 1 of a series of the measurement waveform Y_(t) in step S93,that is, the time point t is larger than the time point 1, the processproceeds to step S94 where the approximate estimation portion 42predicts a particle at the time point t−1 before one time point withrespect to the combinations of states as the particles S_(p) under thestate transition restriction which is a predetermined condition, and theprocess proceeds to step S95.

In step S95, the approximate estimation portion 42 decreases the timepoint t by 1, and the process proceeds to step S96.

In step S96, the evaluation portion 21 obtains the observationprobability P(Y_(t)|S_(p)) of Math (21) with respect to the combinationsof states as the particles S_(p)={S₁, S₂, . . . , and S_(R)} at the timepoint t by using the variance C of the FHMM as the overall models storedin the model storage unit 13, the unique waveform W^((m)), and themeasurement waveform Y_(t)={Y₁, Y₂, . . . , and Y_(T)} from the dataacquisition unit 11. The evaluation portion 21 supplies the observationprobability P(Y_(t)|S_(p)) to the approximate estimation portion 42, andthe process proceeds from step S96 to step S97.

In step S97, the approximate estimation portion 42 obtains the backwardprobability

β_(t,p)

which is in the combinations of states as the particles S_(p) at thetime point t, and, thereafter, that the measurement waveforms Y_(t),T_(t+1), . . . , and Y_(T) are measured by using the observationprobability P(Y_(t)|S_(p)) from the evaluation portion 21, and thetransition probability P^((m)) _(i,j) (and the initial state probability

π^((m)))of the FHMM as the overall modelsφstored in the model storage unit 13, and, the process proceeds to stepS98.

In step S98, the approximate estimation portion 42 samples apredetermined number of particles in descending order of the backwardprobability

β_(t,p)

from the particles S_(p) so as to be left as particles at the time pointt on the basis of the backward probability

β_(t,p).

Thereafter, the process returns to step S93 from step S98, and,hereinafter, the same processes are repeatedly performed.

In addition, it is determined that the time point t is the same as theinitial time point 1 of a series of the measurement waveform Y_(t) instep S93, the process proceeds to step S99.

In step S99, the approximate estimation portion 42 obtains the posteriorprobability

γ_(t,z)

in the combination z of states at the time point t in the FHMM as theoverall models

φ

according to Math (24) by using the forward probability

α_(t,z)(=α_(t,p))

and the backward probability

β_(t,z)(=β_(t,p))

and the process proceeds to step S100.

Hereinafter, in steps S100 and S101, the same processes are in steps S25and S26 of FIG. 7 are performed, and thus the posterior probabilities<S^((m)) _(t)>, <S^((m)) _(t)S^((m)) _(t)′> and <S^((m)) _(t−1)S^((m))_(t)> are obtained.

As described above, in the approximate estimation portion 42, byapplying a particle filter to the combination z of states of the FHMM,the posterior probability <S^((m)) _(t)> and the like are obtained underthe state transition restriction, and therefore calculation forcombination of which a probability is low is omitted, thereby improvingefficiency of calculation of the posterior probability <S^((m)) _(t)>and the like.

Fourth Embodiment of Monitoring System to which Present Technology isApplied

FIG. 21 is a block diagram illustrating a configuration example of thefourth embodiment of the monitoring system to which the presenttechnology is applied.

In addition, in the figure, portions corresponding to the case of FIG. 3are given the same reference numerals, and, hereinafter, descriptionthereof will be appropriately omitted.

The monitoring system of FIG. 21 is common to the monitoring system ofFIG. 3 in that the data acquisition unit 11 to the data output unit 16are provided.

However, the monitoring system of FIG. 21 is different from themonitoring system of FIG. 3 in that a restricted separation learningportion 51 is provided instead of the waveform separation learningportion 31 of the model learning unit 14.

The separation learning portion 31 of FIG. 3 obtains the unique waveformW^((m)) without particular restriction, but, the restricted waveformseparation learning portion 51 obtains the unique waveform W^((m)) undera predetermined restriction distinctive to a household electricalappliance (household electrical appliance separation).

Here, in the separation learning portion 31 of FIG. 3, a particularrestriction is not imposed on calculation of the unique waveform W^((m))in the M step.

For this reason, in the separation learning portion 31 of FIG. 3, theunique waveform W^((m)) is calculated through the waveform separationlearning only in consideration of the measurement waveform Y_(t) being awaveform obtained by superimposing a plurality of current waveforms.

In other words, in the waveform separation learning of the separationlearning portion 31 of FIG. 3, the measurement waveform Y_(t) isregarded as a waveform where a plurality of current waveforms aresuperimposed, and the unique waveform W^((m)) as the plurality ofcurrent waveforms is separated.

In a case where there is no restriction in calculation of the uniquewaveform W^((m)), a degree of freedom (redundancy) of solutions of thewaveform separation learning, that is, a degree of freedom of aplurality of current waveforms which can be separated from themeasurement waveform Y_(t) is very high, and thus the unique waveformW^((m)) as a current waveform which is inappropriate as a currentwaveform of a household electrical appliance may be obtained.

Therefore, the restricted waveform separation learning portion 51performs the waveform separation learning under a predeterminedrestriction distinctive to a household electrical appliance so as toprevent the unique waveform W^((m)) as a current waveform which isinappropriate as a current waveform of a household electrical appliancefrom being obtained, and obtains the unique waveform W^((m)) as anaccurate current waveform of a household electrical appliance.

Here, the waveform separation learning performed under a predeterminedrestriction is also referred to as restricted waveform separationlearning.

The monitoring system of FIG. 21 performs the same processes as themonitoring system of FIG. 3 except that the restricted waveformseparation learning is performed.

In the restricted waveform separation learning, as a restrictiondistinctive to a household electrical appliance, there is, for example,a load restriction or a base waveform restriction.

The load restriction is a restriction where power consumption U^((m)) ofa household electrical appliance which is obtained by multiplication ofthe unique waveform W^((m)) as a current waveform of a householdelectrical appliance #m and a voltage waveform of a voltage applied tothe household electrical appliance #m, that is, the voltage waveformV_(t) corresponding to the measurement waveform Y_(t) which is a currentwaveform does not have a negative value (the household electricalappliance #m does not generate power).

The base waveform restriction is a restriction where the unique waveformW^((m)) which is a current waveform of a current consumed in eachoperating state of the household electrical appliance #m is representedby one or more combinations of a plurality of waveforms prepared inadvance as base waveforms for the household electrical appliance #m.

FIG. 22 is a flowchart illustrating a process of the M step of step S14of FIG. 6 performed by the monitoring system of FIG. 21 imposing theload restriction.

In step S121, the restricted waveform separation learning portion 51performs the waveform separation learning under the load restriction byusing the measurement waveform Y_(t) from the data acquisition unit 11,and the posterior probabilities <S^((m)) _(t)> and <S^((m)) _(t)S^((n))_(t)′> from the estimation portion 22, so as to obtain an update valueW^((m)new) of the unique waveform W^((m)), and updates the uniquewaveform W^((m)) stored in the model storage unit 13 to the update valueW^((m)new), and the process proceeds to step S122.

In other words, the restricted waveform separation learning portion 51solves Math (25) as the waveform separation learning under the loadrestriction by using a quadratic programming with a restriction, therebyobtaining the update value W^((m)new) of the unique waveform W^((m)).

$\begin{matrix}{\mspace{79mu} \left\lbrack {{Math}.\mspace{11mu} 25} \right\rbrack} & \; \\{W^{new} = {{\underset{W}{argmin}\left\{ {\sum\limits_{t = 1}^{T}{{{Y_{t}{\langle S_{t}^{\prime}\rangle}} - {W{\langle{S_{t}S_{t}^{\prime}}\rangle}}}}^{2}} \right\} \mspace{14mu} {subject}\mspace{14mu} {to}\mspace{14mu} 0} \leq {V^{\prime}W}}} & (25)\end{matrix}$

Here, in Math (25), a restriction of satisfying the Math

0≦V′W

as the load restriction is imposed on a unique waveform W which is anupdate value W^((m)new) of the unique waveform W^((m)).

V is a column vector of D rows indicating the voltage waveform V_(t)corresponding to a current waveform as the measurement waveform Y_(t),and V′ is a row vector obtained by transposing the column vector V.

In addition, the update value W^(new) of the unique waveform of Math(25), and the posterior probabilities <S_(t)′> and <S_(t)S_(t)′> are thesame as those described in Math (12).

In other words, the update value W^(new) of the unique waveform is amatrix of D rows and

K×M

columns, and a column vector of (m−1)K+k columns thereof is an updatevalue of a unique waveform W^((m)) _(k) of the state #k of the factor#m.

The posterior probability <S_(t)′> is a row vector of columns,

K×M

and a ((m−1)K+k)-th column component thereof is a state probability thatthe factor #m is in the state #k at the time point t.

The posterior probability <S_(t)S_(t)′> is a matrix of

K×M

rows and

K×M

columns, and a ((m−1)K+k)-th row and ((n−1)K+k′)-th column componentthereof is a state probability that the factor #m is in the state #k andanother factor #n is in the state #k′ at the time point t.

In addition, the unique waveform W of Math (25) is a matrix of D rowsand

K×M

columns which is the same as the update value W^(new) of the uniquewaveform, and a unique waveform W which minimizes X of argmin{X} of theright side of Math (25) is obtained as an update value W^(new) of theunique waveform by using a quadratic programming.

In steps S122 and S123, the same processes as in steps S32 and S33 ofFIG. 9 are performed.

FIG. 23 is a diagram illustrating the load restriction.

In other words, A of FIG. 23 shows a unique waveform W^((m)) as acurrent waveform obtained through the waveform separation learningwithout restriction, and B of FIG. 23 shows a unique waveform W^((m)) asa current waveform obtained through the waveform separation learningunder the load restriction.

In addition, in FIG. 23, the number M of factors is two, and a uniquewaveform W⁽¹⁾ _(k) of the state #k in which the factor #1 is and aunique waveform W⁽²⁾ _(k′) of the state #k′ in which the factor #2 isare shown.

In A of FIG. 23 and B of FIG. 23, the current waveforms which are theunique waveform W⁽¹⁾ _(k) of the factor #1 are all in phase with thevoltage waveform V_(t).

However, in A of FIG. 23, since the load restriction is not imposed, thecurrent waveform which is the unique waveform W⁽²⁾ _(k′) of the factor#2 is in reverse phase to the voltage waveform V_(t).

On the other hand, in B of FIG. 23, as a result of imposing the loadrestriction, the current waveform which is the unique waveform W⁽²⁾_(k′) of the factor #2 is in phase with the voltage waveform V_(t).

The measurement waveform Y_(t) can be separated into the unique waveformW⁽¹⁾ _(k) of the factor #1 and the unique waveform W⁽²⁾ _(k′) of thefactor #2, shown in A of FIG. 23, and can be separated into the uniquewaveform W⁽¹⁾ _(k) of the factor #1 and the unique waveform W⁽²⁾ _(k′)of the factor #2, shown in B of FIG. 23.

However, in a case where the measurement waveform Y_(t) is separatedinto the unique waveform W⁽¹⁾ _(k) of the factor #1 and the uniquewaveform W⁽²⁾ _(k′) of the factor #2, shown in A of FIG. 23, thehousehold electrical appliance #2 corresponding to the factor #2 ofwhich the unique waveform W⁽²⁾ _(k′) is reverse phase to the voltagewaveform V_(t) generates power, and thus there is concern in which it isdifficult to perform appropriate household electrical applianceseparation.

On the other hand, when the load restriction is imposed, the measurementwaveform Y_(t) is separated into the unique waveform W⁽¹⁾ _(k) of thefactor #1 and the unique waveform W⁽²⁾ _(k′) of the factor #2, shown inB of FIG. 23.

The unique waveform W⁽¹⁾ _(k) and the unique waveform W⁽²⁾ _(k′) of B ofFIG. 23 are all in phase with the voltage waveform V_(t), and, thus,according to the load restriction, since the household electricalappliance #1 corresponding to the factor #1 and the household electricalappliance #2 corresponding to the factor #2 are all loads consumingpowers, it is possible to perform appropriate household electricalappliance separation.

FIG. 24 is a diagram illustrating the base waveform restriction.

In FIG. 24, Y is a matrix of D rows and T columns where the measurementwaveform Y_(t) which is a column vector of D rows is arranged from theleft to the right in order of the time point t. A t-th column vector ofthe measurement waveform Y which is a matrix of D rows and T columns isthe measurement waveform Y_(t) at the time point t.

In FIG. 24, W is a matrix of D rows and

K×M

columns in which the unique waveform W^((m)) of the factor #m which is amatrix of D rows and K columns is arranged in order of the factor #mfrom the left to the right. A column vector of (m−1)K+k columns of theunique waveform W which is a matrix of D rows and

K×M

columns is a unique waveform W^((m)) _(k) of the state #k of the factor#m.

In FIG. 24, F indicates

K×M.

In FIG. 24, <S_(e)> is a matrix of

K×M

rows and T columns obtained by arranging a column vector of

F=K×M

rows in which the posterior probability <S^((m)) _(t)> at the time pointt which is a column vector of K rows is arranged in order of the factor#m from the top to the bottom, in order of time point t from the left tothe right. A ((m−1)K+k)-th row and t-th column component of theposterior probability <S_(t)> which is a matrix of

K×M

rows and T columns is a state probability that the factor #m is in thestate #k at the time point t.

In the waveform separation learning, in the FHMM, as shown in FIG. 24, aproduct

W×<S_(t)>

of the unique waveform W and the posterior probability <S_(t)> isobserved as the measurement waveform Y, and thereby the unique waveformW is obtained.

As described above, since the base waveform restriction is a restrictionwhere the unique waveform W^((m)) which is a current waveform of acurrent consumed in each operating state of the household electricalappliance #m is represented by one or more combinations of a pluralityof waveforms prepared in advance as base waveforms for the householdelectrical appliance #m, as shown in FIG. 24, the unique waveform W isindicated by a product

B×A

of a predetermined number N of base waveforms B and a predeterminedcoefficient A, and thereby the unique waveform W is obtained.

Here, when an n-th base waveform B is indicated by B_(n) among apredetermined number N of the base waveforms B, B_(n) is a column vectorof D rows which has, for example, a sample value of a waveform as acomponent, and the base waveform B is a matrix of D rows and N columnsin which the base waveform B_(n) which is a column vector of D rows isarranged in order of the index n from the left to the right.

The coefficient A is a matrix of N rows and

K×M

columns, and, an n-th row and ((m−1)K+k)-th column component is acoefficient which is multiplied by the n-th base waveform B_(n) inrepresenting the unique waveform W^((m)) _(k) as a combination(superimposition) of N base waveforms B₁, B₂, . . . , and B_(N).

Here, when the unique waveform W^((m)) _(k) is represented by acombination of N base waveforms B₁ to B_(N), for example, if a columnvector of N rows which is a coefficient multiplied by the N basewaveforms B₁ to B_(N) is indicated by a coefficient A^((m)) _(k), and amatrix of N rows and K columns in which the coefficient A^((m)) _(k), isarranged in order of K states #1 to #K of the factor #m from the left tothe right is indicated by a coefficient A^((m)), a coefficient A is amatrix in which the coefficient A^((m)) is arranged in order of thefactor #m from the left to the right.

The base waveform B may be prepared (acquired) by performing baseextraction such as, for example, ICA (Independent Component Analysis) orNMF (Non-negative Matrix Factorization) used for an image process, forthe measurement waveform Y.

The measurement wave form may include a sum of electric signals ofelectric apparatus in a house, or may include a group of electricsignals of electric apparatus existing in a house (e.g., a group ofelectrical signals of each electrical apparatus existing in a house).

In addition, in a case where a manufacturer or the like allows a basewaveform of a household electrical appliance to be open to public on ahome page or the like, the base waveform B may be prepared by accessingthe home page.

FIG. 25 is a flowchart illustrating a process of the M step of step S14of FIG. 6 performed by the monitoring system of FIG. 21 imposing thebase waveform restriction.

In step S131, the restricted waveform separation learning portion 51performs the waveform separation learning under the base waveformrestriction by using the measurement waveform Y_(t) from the dataacquisition unit 11, and the posterior probabilities <S^((m)) _(t)> and<S^((m)) _(t)S^((m)) _(t)′> from the estimation portion 22, so as toobtain an update value W^((m)new) of the unique waveform W^((m)), andupdates the unique waveform W^((m)) stored in the model storage unit 13to the update value W^((m)new), and the process proceeds to step S132.

In other words, the restricted waveform separation learning portion 51solves Math (26) as the waveform separation learning under the basewaveform restriction by using a quadratic programming with arestriction, thereby obtaining a coefficient A^(new) for representingthe unique waveform W by a combination of the base waveforms B.

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 26} \right\rbrack & \; \\{A^{new} = {\underset{A}{argmin}\left\{ {\sum\limits_{t = 1}^{T}{{{Y_{t}{\langle S_{t}^{\prime}\rangle}} - {B\; A{\langle{S_{t}S_{t}^{\prime}}\rangle}}}}^{2}} \right\} \mspace{14mu} {subject}\mspace{14mu} {to}\mspace{14mu} \min \mspace{14mu} A}} & (26)\end{matrix}$

Here, in Math (26), to indicating the unique waveform W by a product of

B×A

of a predetermined number N of the base waveforms B and a predeterminedcoefficient A and to minimize the coefficient A (minA) are imposed asthe base waveform restriction.

To minimize the coefficient A (minA) means that a value (magnitude) ofeach component of the matrix of N rows and

K×M

columns which is the coefficient A is minimized, and that the matrix ofN rows and

K×M

columns which is the coefficient A becomes as sparse as possible.

The matrix of N rows and

K×M

columns which is the coefficient A becomes as sparse as possible, andthereby the unique waveform W^((m)) _(k) is indicated by a combinationof the base waveforms B_(n) of which the number is as small as possible.

If the coefficient A^(new) is obtained according to Math (26), therestricted separation learning portion 51 obtains the update valueW^((m)new) of the unique waveform W^((m)) according to Math (27) byusing the coefficient A^(new) and the base waveform B.

[Math.27]

W ^(new) =BA ^(new)  (27)

In steps S132 and S133, the same processes as in steps S32 and S33 ofFIG. 9 are performed.

In a case where current consumed by a household electrical applianceforms waveforms of one or more combinations of base waveforms, awaveform of current which cannot be obtained as current consumed by thehousehold electrical appliance is prevented from being obtained as aunique waveform by imposing the base waveform restriction, and thus itis possible to obtain a unique waveform appropriate for the householdelectrical appliance.

In addition, in the above description, the load restriction and the basewaveform restriction are separately imposed, but the load restrictionand the base waveform restriction may be imposed together.

In a case of imposing the load restriction and the base waveformrestriction together, in the M step, the restricted separation learningportion 51 solves Math (28) as the waveform separation learning underthe load restriction and the base waveform restriction by using aquadratic programming with a restriction, thereby obtaining acoefficient A^(new) for representing the unique waveform W by acombination of the base waveforms B and coefficient A^(new) for makingpower consumption of a household electrical appliance obtained using theunique waveform W not a negative value.

$\begin{matrix}{\mspace{79mu} \left\lbrack {{Math}.\mspace{11mu} 28} \right\rbrack} & \; \\{A^{new} = {{\underset{A}{argmin}\left\{ {\sum\limits_{t = 1}^{T}{{{Y_{t}{\langle S_{t}^{\prime}\rangle}} - {B\; A{\langle{S_{t}S_{t}^{\prime}}\rangle}}}}^{2}} \right\} \mspace{14mu} {subject}\mspace{14mu} {to}\mspace{14mu} \min \mspace{14mu} {A \cdot 0}} \leq {V^{\prime}B\; A}}} & (28)\end{matrix}$

In addition, the restricted waveform separation learning portion 51obtains the update value W^((m)new) of the unique waveform W^((m)) byusing the coefficient A^(new) obtained according to Math (28) and thebase waveform B obtained according to Math (29).

[Math.29]

W ^(new) =BA ^(new)  (29)

Fifth Embodiment of Monitoring System to which Present Technology isApplied

FIG. 26 is a block diagram illustrating a configuration example of thefifth embodiment of the monitoring system to which the presenttechnology is applied.

In addition, in the figure, portions corresponding to the case of FIG. 3are given the same reference numerals, and, hereinafter, descriptionthereof will be appropriately omitted.

The monitoring system of FIG. 26 is common to the monitoring system ofFIG. 3 in that the data acquisition unit 11 to the data output unit 16are provided.

However, first, the monitoring system of FIG. 26 is different from themonitoring system of FIG. 3 in that the individual variance learningportion 52 of FIG. 12 is provided instead of the variance learningportion 32 of the model learning unit 14.

Second, the monitoring system of FIG. 26 is different from themonitoring system of FIG. 3 in that the approximate estimation portion42 of FIG. 15 is provided instead of the estimation portion 22 of thestate estimation unit 12.

Third, the monitoring system of FIG. 26 is different from the monitoringsystem of FIG. 3 in that the restricted separation learning portion 51of FIG. 21 is provided instead of the waveform separation learningportion 31 of the model learning unit 14.

Therefore, the monitoring system of FIG. 26 performs individual variancelearning for obtaining an individual variance

σ^((m))

for each factor #m or for each state #k of each factor #m.

In addition, the monitoring system of FIG. 26 performs approximateestimation for obtaining the posterior probability (stateprobability)<S^((m)) _(t)> under the state transition restriction wherethe number of factors of which a state transitions for one time point isrestricted.

In addition, the monitoring system of FIG. 26 obtains the uniquewaveform W^((m)) through the restricted waveform separation learning.

Further, the monitoring system may perform the individual variancelearning, the approximate estimation, and the restricted waveformseparation learning independently, may perform the individual variancelearning, the approximate estimation, and the restricted waveformseparation learning together, or may perform any two of the individualvariance learning, the approximate estimation, and the restrictedwaveform separation learning.

<Application of Monitoring System to Cases Other than HouseholdElectrical Appliance Separation>

As above, although a case where the monitoring system performinglearning of the FHMM monitors a current waveform as sum total data andperforms household electrical appliance separation has been described,the monitoring system performing learning of the FHMM may be applied toany application where a superposition signal on which one or moresignals are superimposed is monitored and a signal superimposed on thesuperimposition signal is separated.

FIG. 27 is a diagram illustrating an outline of talker separation by themonitoring system which performs learning of the FHMM.

According to the monitoring system which performs learning of the FHMM,instead of a current waveform used as the measurement waveform Y_(t) inthe household electrical appliance separation, by using an audio signalwhere a plurality of talkers' utterances are superimposed, it ispossible to perform talker separation for separating a voice of eachtalker from the audio signal where the plurality of talkers' utterancesare superimposed.

In a case where the monitoring system performing learning of the FHMMperforms the talker separation as well, in the same manner as the caseof the household electrical appliance separation, the individualvariance learning, the approximate estimation, and the restrictedwaveform separation learning may be performed.

A case of using the individual variance

σ^((m))

for each factor #m, or for each state #k of each factor #m in the talkerseparation, a representation performance of the FHMM is further improvedthan a case of using a signal variance C, and thus it is possible toincrease accuracy of the talker separation.

In addition, since a probability that all of utterance states of aplurality of talkers are varied for each time is very low, even if atalker of which an utterance state varies is restricted to one orseveral persons or less for one time point, accuracy of the talkerseparation is not greatly influenced.

Therefore, in the talker separation, under a state transitionrestriction where the number of factors of which a state transitions forone time point is restricted to, for example, one or less (a talker ofwhich an utterance state varies is restricted to one person or less),approximate estimation for obtaining the posterior probability <S^((m))_(t)> may be performed. In addition, according to the approximateestimation for obtaining the posterior probability <S^((m)) _(t)> underthe state transition restriction, the number of combinations z of statesin which the posterior probability

γ_(t,z)

used to obtain the posterior probability <S^((m)) _(t)> is required tobe strictly calculated is considerably reduced, and thus a calculationamount can be notably reduced.

In addition, in the talker separation, the individual waveform W^((m))is a waveform of a person's voice, and thus a frequency component existsin a frequency band which can be taken by a person's voice. Therefore,in restricted waveform separation learning performed in the talkerseparation, a restriction distinctive to a person' voice may be employedin which a frequency component of the individual waveform W^((m)) isrestricted to a frequency component within a frequency band which can betaken by a person's voice. In this case, a unique waveform W^((m)) whichis appropriate as a waveform of a person' voice can be obtained.

Further, in the talker separation, as a restriction imposed on therestricted waveform separation learning, for example, a base waveformrestriction may be employed in the same manner as a case of thehousehold electrical appliance separation.

Sixth Embodiment of Monitoring System to which Present Technology isApplied

FIG. 28 is a block diagram illustrating a configuration example of thesixth embodiment of the monitoring system to which the presenttechnology is applied.

In addition, in the figure, portions corresponding to the case of FIG. 3are given the same reference numerals, and, hereinafter, descriptionthereof will be appropriately omitted.

The monitoring system of FIG. 28 is common to the monitoring system ofFIG. 3 in that the data acquisition unit 11 to the data output unit 16are provided.

However, first, the monitoring system of FIG. 28 is different from themonitoring system of FIG. 3 in that an evaluation portion 71 and anestimation portion 72 are respectively provided instead of theevaluation portion 21 and the estimation portion 22 in the stateestimation unit 12.

Second, the monitoring system of FIG. 28 is different from themonitoring system of FIG. 3 in that a restricted separation learningportion 81 is provided instead of the waveform separation learningportion 31, and the variance learning portion 32 and the state variationlearning portion 33 are not provided, in the model learning unit 14.

The monitoring system of FIG. 3 performs the household electricalappliance separation by using the FHMM as the overall models

φ,

but, the monitoring system of FIG. 28 performs the household electricalappliance separation by using, for example, a model (hereinafter, alsoreferred to as a waveform mode) which has only the unique waveformW^((m)) as a model parameter, as the overall models

φ

instead of the FHMM.

Therefore, in FIG. 28, the waveform mode is stored as the overall models

φ

in the model storage unit 13. Here, the waveform model has the uniquewaveform W^((m)) as a model parameter, and, in this waveform model, asingle unique waveform W^((m)) corresponds to a household electricalappliance model #m.

The state estimation unit 12 has the evaluation portion 71 and theestimation portion 72, and performs state estimation for estimatingoperating states of a plurality of M household electrical appliances #1,#2, . . . , and #m by using the measurement waveform Y_(t) from the dataacquisition unit 11, and the waveform model stored in the model storageunit 13.

In other words, the evaluation portion 71 obtains an evaluation value Ewhere an extent that a current waveform Y supplied from the dataacquisition unit 11 is observed is evaluated so as to be supplied to theestimation portion 72, in each household electrical appliance model #mforming the waveform model as the overall models

φ

stored in the model storage unit 13.

The estimation portion 72 estimates an operating state C^((m)) _(t,k) atthe time point t of each household electrical appliance indicated byeach household electrical appliance model #m by using the evaluationvalue E supplied from the evaluation portion 71, for example, accordingto an integer programming, so as to be supplied to the model learningunit 14, the label acquisition unit 15, and the data output unit 16.

Here, the estimation portion 72 solve an integer programming program ofMath (30) according to the integer programming and estimates theoperating state C^((m)) _(t,k) of the household electrical appliance #m.

$\begin{matrix}{\mspace{79mu} \left\lbrack {{Math}.\mspace{11mu} 30} \right\rbrack} & \; \\{{{minimize}\; E} = {{{{{Y_{t} - {\sum\limits_{m = 1}^{M}{\sum\limits_{k = 1}^{K^{(m)}}{W_{k}^{(m)} \cdot C_{t,k}^{(m)}}}}}}\mspace{14mu} {subject}\mspace{14mu} {to}\mspace{14mu} 0} \leq C_{t,k}^{(m)}} \in_{integer}}} & (30)\end{matrix}$

Here, in Math (30), E indicates an error of the measurement waveformY_(t) and a current waveform)

ΣΣW^((m)) _(k)C^((m)) _(t,k)

which is sum total data observed in the waveform model as the overallmodels

φ,

and the estimation portion 72 obtains an operating state C^((m)) _(t,k)which minimizes the error E.

In addition, in Math (30), the unique waveform W^((m)) _(k) indicates aunique waveform which is a current waveform unique to the operatingstate C^((m)) _(t,k) of the factor #m, and is a column vector of D rowsin the same manner as the measurement waveform Y_(t).

In addition, in Math (30), K(m) indicates the number (the number ofkinds) of operating state C^((m)) _(t,k) of the household electricalappliance #m.

The operating state C^((m)) _(t,k) is an integer of 0 or more and is ascalar value, and indicates an operating state of the householdelectrical appliance #m at the time point t. The operating state C^((m))_(t,k) corresponds to, for example, a mode (setting) of the householdelectrical appliance #m, and, it is assumed that a mode where thehousehold electrical appliance #m is an ON state is restricted to one orless.

Math (31) indicates that a mode where the household electrical appliance#m is an

ON state is restricted to one or less.

[Math.31]

c ^((m)) _(t,1) +c ^((m)) _(t,2) + . . . +c ^((m)) _(t,K(m))≦1  (31)

According to Math (31), a value which the operating state C^((m)) _(t,k)which is an integer of 0 or more can take is 0 or 1.

In addition, a method of estimating an operating state of a householdelectrical appliance according to the integer programming is disclosedin, for example, “Non-intrusive Appliance Load Monitoring System”,Shinkichi Inagaki, Tsukasa Egami, Tatsuya Suzuki (Nagoya University),

Hisahide Nakamura, Koichi Ito (TOENEC CORP.), The 42nd Workshop onDiscrete Event Systems of the Society of Instrument and ControlEngineers, pp. 33-38, Dec. 20, 2008, Osaka University.

As above, in a case of the estimation portion 72 estimates the operatingstate C^((m)) _(t,k) of the household electrical appliance #m by solvingMath (30), the evaluation portion 71 obtains an error E of Math (30) byusing the measurement waveform Y_(t) supplied from the data acquisitionunit 11 and the unique waveform W^((m)) _(k) of the waveform modelstored in the model storage unit 13, so as to be supplied to theestimation portion 72 as an evaluation value E.

In the model learning unit 14, in the same manner as the restrictedwaveform separation learning portion 51 of FIG. 21, the restrictedwaveform separation learning portion 81 performs the waveform separationlearning under a predetermined restriction (waveform separation learningwith restriction) distinctive to a household electrical appliance so asto prevent the unique waveform W^((m)) _(k) as a current waveform whichis inappropriate as a current waveform of a household electricalappliance from being obtained, and obtains the unique waveform W^((m))_(k) as an accurate current waveform of a household electricalappliance.

That is to say, the restricted waveform separation learning portion 81performs the waveform separation learning, for example, under the loadrestriction by using the measurement waveform Y_(t) from the dataacquisition unit 11, and the operating state C^((m)) _(t,k) of thehousehold electrical appliance #m from the estimation portion 72, so asto obtain an update value W^((m)new) _(k) of the unique waveform W^((m))_(k), and updates the unique waveform W^((m)) _(k) stored in the modelstorage unit 13 to the update value W^((m)new) _(k).

In other words, the restricted waveform separation learning portion 81solves the quadratic programming problem of Math (32) as the waveformseparation learning under the load restriction according to thequadratic programming, thereby obtaining the update value W^((m)new)_(k) of the unique waveform W^((m)) _(k).

$\begin{matrix}\left\lbrack {{Math}.\mspace{11mu} 32} \right\rbrack & \; \\{W^{new} = {{\underset{W}{argmin}\left\{ {\sum\limits_{t = 1}^{T}{{Y_{t} - {W \cdot C_{t}}}}^{2}} \right\} \mspace{14mu} {subject}\mspace{14mu} {to}\mspace{14mu} 0} \leq {V^{\prime}W}}} & (32)\end{matrix}$

Here, in Math (32), a restriction of satisfying the math

0≦V′W

as the load restriction is imposed on a unique waveform W which is anupdate value W^((m)new) _(k) of the unique waveform W^((m)) _(k).

In Math (32), V is a column vector of D rows indicating the voltagewaveform V_(t) corresponding to a current waveform as the measurementwaveform Y_(t), and V′ is a row vector obtained by transposing thecolumn vector V.

In addition, if a mode of the household electrical appliance #m of whichthe operating state C^((m)) _(t,k) indicates ON and OFF is a mode #k, inMath (32), W is a column vector of (m−1)K+k columns and is a matrix of Drows and

K×M

columns which is a unique waveform of the mode #k of the householdelectrical appliance #m (is turned on). In addition, here, K indicatesthe maximum value of, for example, K(1), K(2), . . . , and K(M).

The update value W^(new) of the unique waveform is a matrix of D rowsand

K×M

columns, and a column vector of (m−1)K+k columns thereof is an updatevalue of a unique waveform W^((m)) _(k) of the state #k of the factor#m.

Further, in Math (32), C_(t) is a column vector of

K×M

rows in which a component of the (m−1)K+k row is in the operating stateC^((m)) _(t,k).

Further, the restricted waveform separation learning portion 81 mayimpose other restrictions. In other words, for example, in the samemanner as the restricted waveform separation learning portion 51 of FIG.21, the restricted waveform separation learning portion 81 may impose abase waveform restriction instead of the load restriction or may imposeboth the load restriction and the base waveform restriction.

FIG. 29 is a flowchart illustrating a process of learning (learningprocess) of a waveform model performed by the monitoring system of FIG.28.

In step S151, the model learning unit 14 initializes the unique waveformW^((m)) _(k) as the model parameter

φ

of the overall models stored in the model storage unit 13, and theprocess proceeds to step S152.

Here, the respective components of a column vector of D rows which isthe unique waveform W^((m)) _(k) are initialized using, for example,random numbers.

In step S152, the data acquisition unit 11 acquires current waveformscorresponding to a predetermined time T and supplies current waveformsat the respective time points t=1, 2, . . . , and T to the stateestimation unit 12 and the model learning unit 14 as measurementwaveforms Y₁, Y₂, . . . , and Y_(T), and the process proceeds to stepS153.

Here, the data acquisition unit 11 also acquires voltage waveforms alongwith the current waveforms at the time points t=1, 2, . . . , and T. Thedata acquisition unit 11 supplies the voltage waveforms at the timepoints t=1, 2, . . . , and T to the data output unit 16.

In the data output unit 16, the voltage waveforms from the dataacquisition unit 11 are used to calculate power consumption in theinformation presenting process (FIG. 10).

In step S153, the evaluation portion 71 of the state estimation unit 12obtains an error E which is an evaluation value E of Math (30) forobtaining the operating state C^((m)) _(t,k) of each householdelectrical appliance #m by using the measurement waveforms Y₁ to Y_(T)from the data acquisition unit 11, and the unique waveform W^((m)) _(k)of the waveform model stored in the model storage unit 13.

In addition, the evaluation portion 71 supplies the error E to theestimation portion 72, and the process proceeds from step S153 to stepS154.

In step S154, the estimation portion 72 estimates the operating stateC^((m)) _(t,k) of each household electrical appliance #m by minimizingthe error E of Math (30) from the evaluation portion 71, so as to besupplied to the model learning unit 14, the label acquisition unit 15,and the data output unit 16, and the process proceeds to step S155.

In step S155, the restricted waveform separation learning portion 81 ofthe model learning unit 14 performs the waveform separation learningunder a predetermined restriction such as the load restriction by usingthe measurement waveform Y_(t) supplied from the data acquisition unit11 and the operating state C^((m)) _(t,k) of the household electricalappliance #m supplied from the estimation portion 72, thereby obtainingan update value W^((m)new) _(k) of the unique waveform W^((m)) _(k).

In other words, the restricted waveform separation learning portion 81solves Math (32) as the waveform separation learning, for example, undera load restriction, thereby obtaining the update value W^((m)new) _(k)of the unique waveform W^((m)) _(k)

In addition, the restricted waveform separation learning portion 81updates the unique waveform W^((m)) _(k) stored in the model storageunit 13 to the update value W^((m)new) _(k) of the unique waveformW^((m)) _(k), and the process proceeds from step S155 to step S156.

In step S156, the model learning unit 14 determines whether or not aconvergence condition of the model parameter

φ

is satisfied.

Here, as the convergence condition of the model parameter

φ,

for example, a condition in which the estimation of the operating stateC^((m)) _(t,k) in step S154 and the update of unique waveform W^((m))_(k) through the restricted waveform separation learning in step S155are repeatedly performed a predetermined number of times set in advance,or a condition that a variation amount in the error E of Math (30)before update of the model parameter

φ

and after update thereof is in a threshold value set in advance, may beemployed.

In step S156, if it is determined that the convergence condition of themodel parameter

φ

is not satisfied, the process returns to step S153, and, thereafter, theprocesses in steps S153 to S156 are repeatedly performed.

The processes in steps S153 to S156 are repeatedly performed, thus theestimation of the operating state C^((m)) _(t,k) in step S154 and theupdate of unique waveform W^((m)) _(k) through the restricted waveformseparation learning in step S155 are alternately repeated, and therebyaccuracy of an operating state C^((m)) _(t,k) obtained in step S154 and(an update value of) a unique waveform W^((m)) _(k) obtained in stepS155 is increased.

Thereafter, if it is determined that the convergence condition of themodel parameter

φ

is satisfied in step S156, the learning process finishes.

In addition, the state estimation unit 12 may estimate an operatingstate of a household electrical appliance using any other methodsinstead of the integer programming or the FHMM.

<Description of Computer to which Present Technology is Applied>

Meanwhile, the above-described series of processes may be performed byhardware or software. When a series of processes is performed by thesoftware, a program constituting the software is installed in a generalpurpose computer or the like.

Therefore, FIG. 30 shows a configuration example of an embodiment of acomputer in which the program for executing the above-described seriesof processes is installed.

The program may be recorded on a hard disk 105 or a ROM 103 which is arecording medium embedded in the computer.

Alternatively, the program may be stored on a removable recording medium111.

The removable recording medium 111 may be provided as so-called packagesoftware. Here, the removable recording medium 111 includes, forexample, a flexible disc, a CD-ROM (Compact Disc Read Only Memory), anMO (Magneto Optical) disc, a DVD (Digital Versatile Disc), a magneticdisk, a semiconductor memory, and the like.

In addition, the program may be installed into the computer from theremovable recording medium 111 as described above, and further may bedownloaded to the computer via a communication network or a broadcastingnetwork and installed in the hard disk 105. That is to say, the programmay be transmitted to the computer, for example, in a wireless manner,from a download site via an artificial satellite for digital satellitebroadcasting, or may be transmitted to the computer in a wired mannervia a network such as a LAN (Local Area Network) or the Internet.

The computer has a CPU (Central Processing Unit) 102 embedded therein,and the CPU 102 is connected to an input and output interface 110 via abus 101.

When a command is input by a user via the input and output interface 110through an operation of an input unit 107 or the like, the CPU 102executes a program stored in a ROM (Read Only Memory) 103 in responsethereto. Alternatively, the CPU 102 loads a program stored in the harddisk 105 to a RAM (Random Access Memory) 104 so as to be executed.

Thereby, the CPU 102 performs the process according to theabove-described flowchart or the process performed by theabove-described configuration of the block diagram. In addition, the CPU102 outputs or transmits the processed result from an output unit 106 ora communication unit 108 via, for example, the input and outputinterface 110, or records the result on the hard disk 105, as necessary.

In addition, the input unit 107 includes a keyboard, a mouse, amicrophone, or the like. Further, the output unit 106 includes an LCD(Liquid Crystal Display), a speaker, or the like.

Here, in the present specification, a process performed by the computeraccording to the program is not necessarily performed in a time seriesaccording to an order described as a flowchart. In other words, theprocess performed by the computer according to the program also includesa process (for example, a parallel process or a process using objects)performed in parallel or separately.

In addition, the program may be processed by a single computer(processor) or may be processed so as to be distributed by a pluralityof computers. Further, the program may be transmitted to a remotecomputer and be executed.

In addition, in the present specification, the system indicates anassembly of a plurality of constituent elements (devices, modules(components), or the like), and whether or not all the constituentelements are in the same casing is not important. Therefore, both aplurality of devices which are accommodated in separate casings and areconnected to each other via a network, and a single device where aplurality of modules are accommodated in a single casing are a system.

In addition, embodiments of the present technology are not limited tothe above-described embodiments but may have various modificationswithout departing from the scope of the present technology.

For example, the present technology may employ cloud computing where asingle function is distributed to a plurality of devices via a networkand is processed in cooperation.

In addition, each step described in the above flowchart may be not onlyexecuted by a single device, but may be also distributed to a pluralityof devices and be executed.

Further, in a case where a single step includes a plurality ofprocesses, a plurality of processes included in the step may be not onlyexecuted by a single device, but may be also distributed to a pluralityof devices and be executed.

In addition, the present technology may have the followingconfigurations.

(A1) A method for monitoring an electrical device, comprising: obtainingdata representing a sum of electrical signals of two or more electricaldevices, the two or more electrical devices including a first electricaldevice; processing the data with a Factorial Hidden Markov Model (FHMM)to produce an estimate of an electrical signal of the first electricaldevice; and outputting the estimate of the electrical signal of thefirst electrical device, wherein the FHMM has a factor corresponding tothe first electrical device, the factor having three or more states.(A2) The method of (A1), wherein: the three or more states of the factorcorrespond to three or more respective electrical signals of the firstelectrical device in three or more respective operating states of thefirst electrical device.(A3) The method of any of (A1), further comprising obtaining one or moreparameters of the FHMM from a model storage unit.(A4) The method of any of (A1), wherein the data comprises a time seriesof current values and/or a time series of voltage values.(A5) The method of any of (A1), wherein the electrical signal is acurrent signal or a power signal.(A6) The method of any of (A1), further comprising: calculating avariance of the data representing the sum of the electrical signals; andusing the calculated variance as a parameter of the FHMM.(A7) The method of any of (A1), wherein: the FHMM has a second factorcorresponding to a second electrical device of the two or moreelectrical devices; and the method further comprises: processing thedata with the FHMM to produce a second estimate of a second electricalsignal of the second electrical device; calculating a first individualvariance of the estimate of the electrical signal of the firstelectrical device, and using the first individual variance as aparameter of the factor corresponding to the first electrical device;and calculating a second individual variance of the second estimate ofthe second electrical signal of the second electrical device, and usingthe second individual variance as a parameter of the second factorcorresponding to the second electrical device.(A8) The method of any of (A1), further comprising: restricting the FHMMsuch that a number of factors of the FHMM which undergo state transitionat a same time point is less than a threshold number.(A9) The method of any of (A1), further comprising imposing arestriction on the estimate of the electrical signal of the firstelectrical device.(A10) The method of (A9), wherein: the electrical signal is a powersignal; and imposing the restriction comprises restricting the estimateof the power signal of the first electrical device to have anon-negative value.(A11) The method of (A9), wherein imposing the restriction comprisesrestricting the electrical signal to correspond to one or morecombinations of a plurality of predetermined base electrical signals.(A12) The method of any of (A1), wherein processing the data with theFactorial Hidden Markov Model (FHMM) comprises using cloud computing toprocess at least a portion of the data.(A13) The method of any of (A1), wherein outputting the estimate of theelectrical signal comprises displaying the estimate of the electricalsignal.(A14) The method of any of (A1), wherein outputting the estimate of theelectrical signal comprises transmitting the estimate of the electricalsystem to a remote computer.(A15) The method of (A1), wherein the estimate of the electrical signalis an estimate of a voltage signal.(A16) The method of any of (A1), wherein the method is performed by asmart meter.(B1) A monitoring apparatus, comprising: a data acquisition unit forobtaining data representing a sum of electrical signals of two or moreelectrical devices, the two or more electrical devices including a firstelectrical device; a state estimation unit for processing the data witha Factorial Hidden Markov Model (FHMM) to produce an estimate of anoperating state of the first electrical device, the FHMM having a factorcorresponding to the first electrical device, the factor having three ormore states; and a data output unit for outputting an estimate of anelectrical signal of the first electrical device, the estimate of theelectrical signal being based at least in part on the estimate of theoperating state of the first electrical device.(B2) The monitoring apparatus of (B1), further comprising: a modelstorage unit for storing the factor of the FHMM, wherein: the three ormore states of the factor correspond to three or more respectiveelectrical signals of the first electrical device in three or morerespective operating states of the first electrical device.(B3) The monitoring apparatus of any of (B1), further comprising a modelstorage unit for storing one or more parameters of the FHMM.(B4) The monitoring apparatus of any of (B1), wherein the data comprisesa time series of current values and/or a time series of voltage values.(B5) The monitoring apparatus of any of (B1), wherein the estimate ofthe electrical signal is an estimate of a current signal or a powersignal.(B6) The monitoring apparatus of any of (B1), further comprising a modellearning unit for updating a parameter of the FHMM, wherein updating theparameter of the FHMM includes: calculating a variance of the datarepresenting the sum of the electrical signals, and using the calculatedvariance as the parameter.(B7) The monitoring apparatus of any of (B1), further comprising a modellearning unit for updating one or more parameters of the FHMM, wherein:the state estimation unit is for processing the data with the FHMM toproduce a second estimate of a second electrical signal of a second ofthe two or more electrical devices, the FHMM having a second factorcorresponding to the second electrical device; and updating the one ormore parameters of the FHMM includes: calculating a first individualvariance of the estimate of the electrical signal of the firstelectrical device, and using the first individual variance as aparameter of the factor corresponding to the first electrical device;and calculating a second individual variance of the second estimate ofthe second electrical signal of the second electrical device, and usingthe second individual variance as a parameter of the second factorcorresponding to the second electrical device.(B8) The monitoring apparatus of any of (B1), wherein processing thedata comprises restricting the FHMM such that a number of factors of theFHMM which undergo state transition at a same time point is less than athreshold number.(B9) The monitoring apparatus of (B1), wherein processing the datacomprises imposing a restriction on the estimate of the electricalsignal of the first electrical device.(B10) The monitoring apparatus of (B9), wherein: the electrical signalis a power signal; and imposing the restriction comprises restrictingthe estimate of the power signal of the first electrical device to havea non-negative value.(B11) The monitoring apparatus of (B9), wherein imposing the restrictioncomprises restricting the electrical signal to correspond to one or morecombinations of a plurality of predetermined base electrical signals.(B12) The monitoring apparatus of any of (B1), wherein processing thedata with the Factorial Hidden Markov Model (FHMM) comprises using cloudcomputing to process at least a portion of the data.(B13) The monitoring apparatus of any of (B1), wherein outputting theestimate of the electrical signal comprises displaying the estimate ofthe electrical signal.(B14) The monitoring apparatus of any of (B1), further comprising acommunication unit, wherein outputting the estimate of the electricalsignal comprises transmitting the estimate of the electrical system to aremote computer.(B15) The monitoring apparatus of (B1), wherein the estimate of theelectrical signal is an estimate of a voltage signal.(B16) The monitoring apparatus of any of (B1), wherein the monitoringapparatus is a smart meter.(C1) A monitoring apparatus, comprising: a data acquisition unit forobtaining data representing a sum of electrical signals of two or moreelectrical devices, the two or more electrical devices including a firstelectrical device; a state estimation unit for processing the data witha Factorial Hidden Markov Model (FHMM) to produce an estimate of anoperating state of the first electrical device, the FHMM having a factorcorresponding to the first electrical device, the factor having three ormore states; a model learning unit for updating one or more parametersof the FHMM, wherein updating the one or more parameters of the FHMMcomprises performing restricted waveform separation learning; and a dataoutput unit for outputting an estimate of an electrical signal of thefirst electrical device, the estimate of the electrical signal beingbased at least in part on the estimate of the operating state of thefirst electrical device.(C2) The monitoring apparatus of (C1), wherein performing restrictedwaveform separation learning comprises imposing a restrictiondistinctive to the first electrical device on a value of a correspondingparameter of the FHMM.(C3) The monitoring apparatus of (C2), wherein the restriction comprisesa load restriction.(C4) The monitoring apparatus of any of (C2), wherein the restrictioncomprises a base waveform restriction.(D1) A data processing apparatus, comprising:a data acquisition unit configured to obtain data representing a mixedsignal of two or more time series signals;a state estimation unit configure to estimate a parameter for modeling atime series signal with a stochastic dynamics model;wherein the state estimation unit is configured to estimate theparameter under a specific restriction.(D2) A data processing apparatus of (D1),wherein the stochastic dynamics model is FHMM (Factorial Hidden MarkovModel).(D3) A data processing apparatus of (D1),wherein the state estimation unit is configured to estimate theparameter under the specific restriction by controlling the data to beadditive data of positive factors.(D4) A data processing apparatus of (D1),wherein the state estimation unit is configured to estimate theparameter under the specific constraint controlling at least one stateof factors to output zero signals.(D5) A data processing apparatus of (D2),wherein the state estimation unit is configured to estimate theparameter under the specific constraint controlling to minimize theprobability of selecting non-zero state of factors.(D6) A data processing apparatus of (D1),wherein the state estimation unit is configured to estimate theparameter under the specific restriction by restricting the FHMM suchthat a number of factors of the FHMM (Factorial Hidden Markov Model)which can undergo state transition at a same time point is less than athreshold number.(D7). A data processing apparatus of (D1),wherein the state estimation unit is configured to estimate theparameter under the specific restriction by restricting the estimatedparameter to one or more combinations of a plurality of predeterminedparameters.(D8) A data processing apparatus of (D1),wherein the state estimation unit is configured to estimate theparameter under the specific restriction by restricting the estimatedparameter to one or more combinations of a plurality of time seriessignals.(D9) The data processing apparatus of (D2), wherein the state estimationunit is configured to estimate the parameter under the specificrestriction by:calculating a variance of the data representing the mixed signal of thetwo or more time series signals; andusing the calculated variance as a parameter of the FHMM (FactorialHidden Markov Model).(E1) A data processing apparatus including a state estimation unit thatperforms state estimation for obtaining a state probability which is ina state of each factor of an FHMM (Factorial Hidden Markov Model) byusing sum total data regarding a sum total of current consumed by aplurality of electrical appliances; and a model learning unit thatperforms learning of the FHMM of which each factor has three or morestates by using the state probability.(E2) The data processing apparatus set forth in (E1), wherein the FHMMincludes as model parametersa unique waveform unique to each state of each factor, used to obtain anaverage value of observation values of the sum total data observed incombinations of states of the respective factors; a variance ofobservation values of the sum total data observed in the combinations ofstates of the respective factors; an initial state probability that astate of each factor is an initial state; and a transition probabilitythat a state of each factor transitions, and wherein the model learningunit includes a waveform separation learning portion that performswaveform separation learning for obtaining the unique waveform; avariance learning portion that performs variance learning for obtainingthe variance; and a state variation learning portion that performs statevariation learning for obtaining the initial state probability and thetransition probability.(E3) The data processing apparatus set forth in (E2), wherein thevariance learning portion obtains an individual variance for eachfactor, or an individual variance for each state of each factor.(E4) The data processing apparatus set forth in (E2) or (E3), whereinthe state estimation unit obtains the state probability under a statetransition restriction where the number of factors of which a statetransitions for one time point is restricted.(E5) The data processing apparatus set forth in (E4), wherein the stateestimation unit obtains an observation probability that the sum totaldata is observed in the combinations of states of the respective factorsby using the average value and the variance; a forward probability□t,zthat the sum total data Y1, Y2, . . . , and Yt is observed and which isin a combination z of states of the respective factors at the time pointt, and a backward probability□t,zwhich is in the combination z of states of the respective factors at thetime point t and thereafter that sum total data Yt, Yt+1, . . . , and YTis observed, with respect to a series Y1, Y2, . . . , and YT of the sumtotal data, by using the observation probability and the transitionprobability; a posterior probability□t,zwhich is in the combination z of states of the respective factors at thetime point t by using the forward probability□t,zand the backward probability□t,z;and the state probability by marginalizing the posterior probability□t,z,and wherein the state estimation unit, by using a combination of statesof the respective factors as a particle, predicts a particle after onetime point under the state transition restriction, and obtains theforward probability□t,zof the combination z of states as the particle while repeating samplingof a predetermined number of particles on the basis of the forwardprobability□t,z;and predicts a particle before one time point under the state transitionrestriction, and obtains the backward probability□t,zof the combination z of states as the particle while repeating samplingof a predetermined number of particles on the basis of the backwardprobability□t,z.(E6) The data processing apparatus set forth in any one of (E2) to (E5),wherein the waveform separation learning portion obtains the uniquewaveform under a restriction distinctive to the electrical appliance.(E7) The data processing apparatus set forth in (E6), wherein thewaveform separation learning portion obtains the unique waveform under aload restriction where power consumption of the electrical applianceobtained using the unique waveform does not have a negative value.(E8) The data processing apparatus set forth in (E6), wherein thewaveform separation learning portion obtains the unique waveform under abase waveform restriction where the unique waveform is represented byone or more combinations of a plurality of base waveforms prepared forthe electrical appliances.(E9) A data processing method including the steps of performing stateestimation for obtaining a state probability which is in a state of eachfactor of an FHMM (Factorial Hidden Markov Model) by using sum totaldata regarding a sum total of current consumed by a plurality ofelectrical appliances; and performing learning of the FHMM of which eachfactor has three or more states by using the state probability.(E10) A program causing a computer to function as a state estimationunit that performs state estimation for obtaining a state probabilitywhich is in a state of each factor of an FHMM (Factorial Hidden MarkovModel) by using sum total data regarding a sum total of current consumedby a plurality of electrical appliances; and a model learning unit thatperforms learning of the FHMM of which each factor has three or morestates by using the state probability.

REFERENCE SIGNS LIST

-   -   11 DATA ACQUISITION UNIT    -   12 STATE ESTIMATION UNIT    -   13 MODEL STORAGE UNIT    -   14 MODEL LEARNING UNIT    -   15 LABEL ACQUISITION UNIT    -   16 DATA OUTPUT UNIT    -   21 EVALUATION PORTION    -   22 ESTIMATION PORTION    -   31 WAVEFORM SEPARATION LEARNING PORTION    -   32 VARIANCE LEARNING PORTION    -   33 STATE VARIATION LEARNING PORTION    -   42 ESTIMATION PORTION    -   51 RESTRICTED WAVEFORM SEPARATION LEARNING PORTION    -   52 INDIVIDUAL VARIANCE LEARNING PORTION    -   71 EVALUATION PORTION    -   72 ESTIMATION PORTION    -   81 RESTRICTED WAVEFORM SEPARATION LEARNING PORTION    -   101 BUS    -   102 CPU    -   103 ROM    -   104 RAM    -   105 HARD DISK    -   106 OUTPUT UNIT    -   107 INPUT UNIT    -   108 COMMUNICATION UNIT    -   109 DRIVE    -   110 INPUT AND OUTPUT INTERFACE    -   111 REMOVABLE RECORDING MEDIUM

1. A data processing apparatus, comprising: a data acquisition unitconfigured to obtain data representing a mixed signal of two or moretime series signals; a state estimation unit configure to estimate aparameter for modeling a time series signal with a stochastic dynamicsmodel; wherein the state estimation unit is configured to estimate theparameter under a specific restriction.
 2. The data processing apparatusof claim 1, wherein the stochastic dynamics model is FHMM (FactorialHidden Markov Model).
 3. The data processing apparatus of claim 1,wherein the state estimation unit is configured to estimate theparameter under the specific restriction by controlling the data to beadditive data of positive factors.
 4. The data processing apparatus ofclaim 1, wherein the state estimation unit is configured to estimate theparameter under the specific constraint controlling at least one stateof factors to output zero signals.
 5. The data processing apparatus ofclaim 1, wherein the state estimation unit is configured to estimate theparameter under the specific constraint controlling to minimize theprobability of selecting non-zero state of factors.
 6. The dataprocessing apparatus of claim 2, wherein the state estimation unit isconfigured to estimate the parameter under the specific restriction byrestricting the FHMM such that a number of factors of the FHMM(Factorial Hidden Markov Model) which can undergo state transition at asame time point is less than a threshold number.
 7. The data processingapparatus of claim 1, wherein the state estimation unit is configured toestimate the parameter under the specific restriction by restricting theestimated parameter to one or more combinations of a plurality ofpredetermined parameters.
 8. The data processing apparatus of claim 1,wherein the state estimation unit is configured to estimate theparameter under the specific restriction by restricting the estimatedparameter to one or more combinations of a plurality of time seriessignals.
 9. The data processing apparatus of claim 2, wherein the stateestimation unit is configured to estimate the parameter under thespecific restriction by: calculating a variance of the data representingthe mixed signal of the two or more time series signals; and using thecalculated variance as a parameter of the FHMM (Factorial Hidden MarkovModel).